uk: Комп'ютерні науки

description: Lessons learned in X years of parallel programming [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 2] [Rimini, Italy] There is a lot more to parallel programming in Python than multiprocessing.Pool().map. In this talk I will share some hard-learned knowledge gained in several years of parallel programming. Covered topics will include performance, ways to measure the performance, memory occupation, data transfer and ways to reduce the data transfer, how to debug parallel programs and useful libraries. I will give some practical examples, both in enterprise programming (importing CSV files in a database) and in scientific programming (numerical simulations). The initial part of the talk will be pedagogical, advocating the convenience of parallel programming in the small (i.e. in single machine environment); the second part will be more advanced and will touch a few things to know when writing parallel programs for medium-sized clusters. I will also briefly discuss the compatibility layer that we have developed at GEM to be independent from the underlying parallelization technology (multiprocessing, concurrent.futures, celery, ipyparallel, grid engine...)

uk: вхід без авторизації


Python Microservices

Miguel Grinberg

type: Person

cs: Informatika

de: Informatik

fi: tietojenkäsittelytiede

uk: Комп'ютерні науки

en: Computer Science

fr: Informatique

da: Datalogi

es: Ciencias de la Computación

nl: Informatica

cs: Technické vědy

de: Ingenieurwissenschaften

fi: tekniikka

uk: Інженерія

en: Engineering Sciences

fr: Ingénierie

da: Ingeniørvidenskab

es: Ingeniería

nl: Engineering

description: Python Microservices [EuroPython 2017 - Talk - 2017-07-10 - Anfiteatro 2] [Rimini, Italy] This talk is for Python web developers interested in learning what are the core ideas behind microservices, what problems they try to solve, and what are the viable options to implement them in Python, both from technical and teamwork point of views. Some of the topics that will be discussed include the role of APIs, the improvements microservices bring to application scalability, upgrades, and maintenance, and the challenges in breaking up a monolithic application. Attendees will leave the talk with a good idea of what microservices are, and how they help build better applications. As usual with my presentations, there will be actual code examples presented during the talk that I will also host on GitHub, for those interested in investigating further on their own

TIB AV-Portal

type: Service

type: WebContent

type: MediaObject

@language: en

cs: přihlášení není vyžadováno

de: keine Anmeldung erforderlich

uk: вхід без авторизації

en: no login required

nl: geen login vereist

EuroPython

type: Organization

cs: Video

de: Video

fi: Video

uk: Відео

en: Video

fr: Vidéo

da: Video

nl: Video

es: Video


uk: Комп'ютерні науки

description: Teach your (micro)services speak Protocol Buffers with gRPC. [EuroPython 2017 - Talk - 2017-07-12 - PythonAnywhere Room] [Rimini, Italy] When it comes to microservices, there're a lot of things worth keeping in mind. Designing such fine-grained, loosely-coupled services requires paying lots of attention to various patterns and approaches to make them future-proof. A very important thing to consider, is the way those services will communicate with each-other in production. Usually the communication is done over the network using a technology-agnostic protocol. At the next level the service should provide an API for its friend services. Then, the data should be serialized without altering its meaning and transferred to the picked endpoint. Nowadays, exposing a REST API that operates with JSON over plain HTTP is a usual way to lay the grounds of communication for the services. It is easy to accomplish, but it has some drawbacks. First of all, JSON is a human readable format, and it’s not as other serialization approaches. Also, with JSON it’s not possible to natively enforce the schema, and evolving the API may be painful. This talk’s purpose is to describe in deep detail the benefits of protocol buffers, that offer us for free an easy way to define the API messages in the proto format, and then reuse them inside different services, without even being locked to use the same programming language for them. Moreover, with gRPC we can define the API’s endpoints easily in the same proto format. All these offer us a robust schema enforcement, compact binary serialization, and easy backward compatibility

uk: вхід без авторизації


Programming in Parallel with Threads

uk: Комп'ютерні науки

description: Programming in Parallel with Threads [EuroPython 2017 - Talk - 2017-07-11 - PyCharm Room] [Rimini, Italy] Threads are typically not the way to take advantage of multiple CPUs for CPU-bound problems. The Global Interpreter Lock (GIL) allows the use of only one CPU at the time when using threads. However, the GIL is released for IO task The use case is a scientific simulation model that has to run 18,000 different simulations. The input data for these simulations need to be extracted from a common database, re-assembled and translated into several input files per simulation. After each simulation that is run with an external, standalone executable, the output data needs to be gathered and rearranged in a output database. The implementation scaled up to 50 threads. On a eight-core machine more than 90 % usage of all CPUs could be achieved, bringing the total run time down to about two hours from about 15 hours. Depending on the use case, threading can help to speedup a program and even take advantage of multiple CPUs. This talk presents such a use case. The approach can be translated to problems from other domains if the sub-tasks can be turned into IO tasks. Asynchronous programming could have been used here. However using a thread per task and using class that represents a task, is likely conceptually simpler for programmers not used to asynchronous programming

uk: вхід без авторизації


Python on Windows, Like a Boss

uk: Комп'ютерні науки

description: Python on Windows, Like a Boss [EuroPython 2017 - Talk - 2017-07-10 - Arengo] [Rimini, Italy] Historically, Python coding on Windows has been kind of troublesome, but not anymore. Since a couple of years ago it is possible write awesome quality Python code with Visual Studio. This is good news for Python developers of all levels, because Visual Studio offers and incredible set of tools to enhance all the stages of product development: from managing virtual environments and handling package management, to writing and refactoring code, up to testing & debugging - all of that for free. In this talk I will demo how you can use Visual Studio to boost your Python development on Windows

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Explaining behavior of Machine Learning models with eli5 library [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] ML estimators don't have to be black boxes. Interpretability has many benefits: it is easier to debug interpretable models, humans trust decisions of such models more. In this talk I’ll give an overview of ML models interpretation and debugging techniques. I’ll cover linear models, decision trees, tree ensembles, arbitrary classifiers using LIME algorithm. The talk focus is on explanation algorithms, because it is important to be aware of pitfalls and limitations of the explanation method to be able to interpret an explanation correctly. I’ll also show how to use these techniques in practice, to debug and explain behavior of estimators from Python ML libraries like scikit-learn and xgboost using open-source eli5 library: https://github.com/TeamHG-Memex/eli5 . Attendees will get both practical and theoretical understanding of these explanation methods. Target audience is ML practitioners who want to 1) get a better quality from their ML pipelines - understanding of why a wrong decision happens is often a first step to improve the quality of an ML solution; 2) explain ML model behavior to clients or stakeholders - inspectable ML pipelines are easier to “sell” to a client; humans trust such models more because they can check if an explanation is consistent with their domain knowledge or gut feeling, understand better shortcomings of the solution and make a more informed decision as a result

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Graph Databases: Talking about your Data Relationships with Python [EuroPython 2017 - Talk - 2017-07-14 - PyCharm Room] [Rimini, Italy] Have you ever considered how many relationships you have in your virtual life? Every friend or page liked on Facebook, each connection in LinkedIn or Twitter account followed is a new relationship not only between two people, but also between their data. In Brazil only, we have 160 millions Facebook users. How can we represent and manipulate all these relationships? Graph Databases are storage systems that use graph structure (nodes and edges) to represent and store data in a semantic way. This talk will begin approaching the challenge in representing relationships in Relational Databases and introducing a more friendly solution using graph. The definition of Graph Database, its pros and cons and some available tools (Neo4J, OrientDB and TitanDB) will be shown during the presentation, as well as how these tools can be integrated with Python. Outline: Relationships Relationships in Relational Databases Graph Definition Graph approach to represent relationships Graph Databases Definition Advantages Neo4J Usage Examples Integration with Python Comparison between Graph Databases Comparison between Neo4J and Relational Database Application

uk: вхід без авторизації


uk: Комп'ютерні науки

description: When Django is too bloated - Specialized Web-Applications with Werkzeug [EuroPython 2017 - Talk - 2017-07-13 - PythonAnywhere Room] [Rimini, Italy] Did you ever think, Django and all the other “batteries included” frameworks are not flexible enough for your needs? Do you feel like they limit you in your creativity and design? Then this talk is for you! Werkzeug is a very lightweight HTTP/WSGI utility for Python. You might have actually used it before, since the popular framework Flask is based on it. Werkzeug handles the WSGI communication with the web server and parsing of HTTP packets for you, after that, you are left to do whatever you want. No pre-defined ORM, no request dispatching or template rendering. As a developer you are supported with a live debugger that runs in the browser and a great variety of testing tools making it easy to write fine grained unit tests for your application. As a developer at MPS - Medical Systems, I work with Werkzeug on a daily basis. One of our products is ChemoCompile, a chemo therapy planning, management and documentation tool used in hospitals in various European countries. It is a single-page web application written in Python (backend) and AngularJS (frontend). When we created it, we first prototyped it using Django, but soon realized, that we did not need most of the functionality that Django provides and many of our needs, like interfacing with hospital information systems, are too much out of the scope of a regular web applications. I will talk about, how we then discovered Werkzeug and built our own very customized stack on top of it and how you can do it too

uk: вхід без авторизації


uk: Комп'ютерні науки

description: How Facebook uses Python to build (and operate) datacenters at scale [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 1] [Rimini, Italy] With 4 datacenters on-line and more coming fast, building and operating datacenter buildings becomes a problem we need to solve at scale. At Facebook, Several teams of Production Engineers write the software that helps us do this efficiently, and we use Python... a lot. In this talk, I will go into some detail about only some of problems we try to solve to make sure our datacenters come online on time so that we can make sure you can connect with all your friends on Facebook, and keep them humming, as efficiently as possible. We'll go into some detail about the awesome Python infrastructure (some of it open source), that we use to build this software, and some of the engineering practices. This is a talk for you if you were wondering how to track each and every strand of fiber cabling within a datacenter, or make sure we find out that the cooling system isn't really doing it's thing before actual servers catch fire from serving you live videos

uk: вхід без авторизації


Python and Angular, a perfect match?

uk: Комп'ютерні науки

description: Python and Angular, a perfect match? [EuroPython 2017 - Talk - 2017-07-14 - PyCharm Room] [Rimini, Italy] Python on mobile devices is still negligible mostly because of the GUI problems which arise. But Python on android devices is considered to be a solved problem since the module Python for Android is available. We have nowadays at least the possibility to develop for mobile devices with the GUI engine Kivy which is maybe the first choice for rapid prototyping on that platform. But as it comes to design work and mobile device look alike Kivy is still far behind the web development tools HTML/CSS. In our talk we present a conceptual work where we used Ionic - this is an mobile development framework based on Angular - to build the GUI part of an app and connected that to a Python back end. In our point of view our proposal is very general and will give Python a boost towards modern UX development and makes HTML/CSS/JavaScript a real option especially in combination with Angular. The main part of our solution that we show is the interoperation between JavaScript and Python such that asynchronous calls in both direction are possible. The advantage is to develop UX and back end code only once and use it literally on every platform where a Python interpreter and a browser runs

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Introduction to Nonparametric Bayesian Models [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 1] [Rimini, Italy] When we use supervised machine learning techniques we need to specify the number of parameters that our model will need to represent the data (number of clusters, number of Gaussians, etc.). Somewhat, we are making our model inflexible. In this talk we will study the nonparametric models, in specific, Bayesian Nonparametric Models (BNP) whose main purpose is getting more flexible models since that in BNP the parameters can be automatically inferred by the model. The outline is the next: Parametric vs Nonparametric models A review on probability distributions Non-parametric Bayesian Methods Dirichlet Process Python (and R maybe) libraries for NPB Conclusion

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Full-Text Search in Django with PostgreSQL [EuroPython 2017 - Talk - 2017-07-12 - PythonAnywhere Room] [Rimini, Italy] After some experiences in the implementation of full-text search functionality with different system, we have decided to use PostgreSQL to implement full-text search functionality in our next project, a website to search for shows, venues, bands and festivals. In the past, I have worked in two different projects, a mobile platform to sell and buy used items and a sport videos sharing platform, where I used two of the most currently famous full-text search software (Elasticsearch or Solr) but I had some synchronization and management problems. After that, in my company, we searched for new Django support of full-text search PostgreSQL implementation and we decided to use it to avoid any problems that I had in the past. I’m going to start speaking about the full-text search in a general context and I want to show the problems I encountered implementing it in the past. Afterwards, I’m going to talk about the PostgreSQL functionality to implement the full-text search functionality and also present the django.contrib.potgres.search module, with step-by-step demonstrations of its functions with real world data. Finally, I’m going to show the way we use and test this functionality in our project and which functionality lacks us to have a complete implementation of full-text search in our project. At the end, I want to present my conclusions about our solution and I want to explore some new features that will be present in the next versions of Django and PostgreSQL

uk: вхід без авторизації


Django and Graphql

uk: Комп'ютерні науки

description: Django and Graphql [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 1] [Rimini, Italy] The web is constantly evolving, that is even more true with the frontend world. You don’t have anymore the traditional webapp, in fact you now have two apps, backend and frontend. But how do they communicate? Traditionally we have always created REST APIs, but now, there’s a new player. GraphQL

uk: вхід без авторизації


An introduction to PyTorch & Autograd

uk: Комп'ютерні науки

description: An introduction to PyTorch & Autograd [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] PyTorch is an optimized tensor library for Deep Learning, and is a recent newcomer to the growing list of GPU programming frameworks available in Python. Like other frameworks it offers efficient tensor representations and is agnostic to the underlying hardware. However, unlike other frameworks it allows you to create ""define-by-run"" neural networks resulting in dynamic computation graphs, where every single iteration can be different---opening up a whole new world of possibilities. Central to all neural networks in PyTorch is the Autograd package, which performs Algorithmic Differentiation on the defined model and generates the required gradients at each iteration. In this talk I will present a gentle introduction to the PyTorch library and overview its main features using some simple examples, paying particular attention to the mechanics of the Autograd package. Keywords: GPU Processing, Algorithmic Differentiation, Deep Learning, Linear algebra

uk: вхід без авторизації


A faster Python? You Have These Choices

uk: Комп'ютерні науки

description: A faster Python? You Have These Choices [EuroPython 2017 - Talk - 2017-07-13 - Arengo] [Rimini, Italy] Python was never intended as a fast language but many modern uses of Python require high performance computing, particularly in data science. This talk explores your options for squeezing maximum performance out of critical Python code. This talk provides a succinct summary of the options you have: C extensions, Cython, CFFI, PyPy and many others. It also shows the trade-offs between execution performance and the cost of writing and maintaining code with each choice. Each option is also explored for maturity and ease of use for Python programmers. A real world programming problem is coded and benchmarked using each of these techniques. All the code used in the talk is available on GitHub. At the end of this talk you will be better place to decide on which technique to use to make your code run 100x faster

uk: вхід без авторизації


Improve your developer's toolset

uk: Комп'ютерні науки

description: Improve your developer's toolset [EuroPython 2017 - Talk - 2017-07-11 - PythonAnywhere Room] [Rimini, Italy] For Python development, we mainly focus on IDE, browser and sometimes a command line. I want to focus on command line tools which seem to be a bit forgotten these days. Let me show a few tools I'm using on daily basis and how they helped me become more productive. I'll prove it's possible to start up a new environment from scratch that feels like $HOME. Finally, I want to convince you how important it is to constantly challenge yourself and your tools, how useful is a custom toolbox tailored to you

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Infrastructure as Python Code: Run your Services on Microsoft Azure [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 1] [Rimini, Italy] Using Infrastructure-as-Code principles with configuration through machine processable definition files in combination with the adoption of cloud computing provides faster feedback cycles in development/testing and less risk in deployment to production. The Microsoft Azure Cloud (https://azure.microsoft.com/) allows different ways to provision, deploy and run your python service: The Azure Resource Manger Templates (https://azure.microsoft.com/en-us/resources/templates/) allows you to provision your application using a declarative template. With parameters, variables and Azure template functions, the same template can be used to deploy your application in different stages (dev, test, production) and environments for different customers. We open sourced the tropo library (https://pypi.python.org/pypi/tropo/) to create Azure Resource Templates from python. Azure SDK for Python (http://azure-sdk-for-python.readthedocs.io) for a low level access to manage resources in the Azure Cloud. An Azure Ansible Module (https://docs.ansible.com/ansible/guide_azure.html) based on the Azure SDK to automate software provisioning, configuration management, and application deployment in a single environment. Each of the alternatives has different strengths and drawbacks. Presenting our learnings from migrating our infrastructure into the Azrue Cloud will help to avoid common pitfalls and show deployment patterns that will ease the live of devops

uk: вхід без авторизації


How to use pandas the wrong way

uk: Комп'ютерні науки

description: How to use pandas the wrong way [EuroPython 2017 - Talk - 2017-07-12 - Anfiteatro 1] [Rimini, Italy] UPDATE: slides and materials can be found at http://pietrobattiston.it/python:pycon#europython_rimini_july_2017 The pandas library represents a very efficient and convenient tool for data manipulation, but sometimes hides unexpected pitfalls which can arise in various and sometimes unintelligible ways. By briefly referring to some aspects of the implementation, I will review specific situations in which a change of approach can make code based on pandas more robust, or more performant. Some examples: inefficient indexing multiple dtypes and efficiency implicit type casting HDF5 storage overhead GroupBy.apply()... when you don't actually need i

uk: вхід без авторизації


CyberSecurity.bootcamp()

uk: Комп'ютерні науки

description: CyberSecurity.bootcamp() [EuroPython 2017 - Talk - 2017-07-11 - Arengo] [Rimini, Italy] Cyber-security is a critical part of all distributed applications. By understanding and implementing proper security measures, you guard your own resources against malicious attackers as well as provide a secure environment for all relevant parties. The purpose of the talk is to show starting points on how to improve security in python applications by destroying a few servers during the presentation. It will provide the most important information and will cover: Threat modeling Common attack vectors on Python applications. Why python is not vulnerable to some kinds of attacks. Why is eval so dangerous? Improving server deployment and security management. Automated security testing. Pentesting. Who is a CISO and why is cyber-security awareness in the company so important nowadays. Basic knowledge of networking, python and REST is advised

uk: вхід без авторизації


MicroPython Workshop

uk: Комп'ютерні науки

description: MicroPython Workshop [EuroPython 2017 - Interactive session - 2017-07-10 - Anfiteatro 2] [Rimini, Italy] We will program some ESP8266-based development boards, make them blink LEDs, move servos, display pictures, read sensors, react to buttons, and, more importantly, talk over HTTP and MQTT with servers. This is an excellent occasion to try your skills in building and programming small electronic devices. No experience with electronics is required, everything you need to know will be covered during the workshop. You will need to bring a laptop with a USB port. If it runs Windows, make sure to have some terminal emulator installed, such as PuTTy, CoolTerm or even HyperTerm. On Linux and OSX we will use Screen, so you don't need anything special. I only have about 20 kits for this, so the number of people that can take part is limited

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Practical Debugging - Tips, Tricks and Ways to think [EuroPython 2017 - Talk - 2017-07-14 - Arengo] [Rimini, Italy] When we write code, oftentimes things are not working as expected. We followed the tutorial and yet we got an error. We introduce a new feature but something else broke. Having to deal with bugs is inevitable. Trying to ""debug"" what happened or what caused the problem can be really frustrating and timewasting. The aim of this talk is go give ways to think & steps to take when we are faced with the process of debugging. The language for the examples is going to be Python & the tools used - from the python ecosystem. The talk will be practical, with a lot of real-world examples. The goal is to cover the following scenarios by showing different ways to approch the problem: You followed a tutorial but it's not working. What to do? You introduce new feature but things broke somewhere else in the project. What to do? You are using a popular 3rd party library but something breaks. What to do? A bug occurs and you have no idea what or who caused it. What to do? You can't fix or find the bug. What now? You want to generalize your debugging skills. How to do that? We will be talking about critical changes, binary search, problem isolation, interactive debuggers, printing, testing, greping and other interesting things

uk: вхід без авторизації


Python REST frameworks review

uk: Комп'ютерні науки

description: Python REST frameworks review [EuroPython 2017 - Talk - 2017-07-10 - PyCharm Room] [Rimini, Italy] Number of libraries for building RESTful web applications is growing up. Selecting best framework becomes hard decision for web developers. During the presentation I will go through best libraries I can recommend for building RESTful web applications. Selection of libraries will be subjective, based on my experience with building microservices in Python. I will share code examples, weaknesses and strengths for each library. I hope everyone will find something that best suits his needs

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Hi, my name is README! - A Look at Why Docs are So Important [EuroPython 2017 - Talk - 2017-07-12 - PyCharm Room] [Rimini, Italy] When starting a new project, as developers we usually get right into hacking things, like tinkering with libs that we would like to learn or solving a particular problem as quickly a possible. Occasionally we also decide to publish the resulting package to PyPI, so that others can use our nifty code, submit a pull request and maybe even form a community around the project. If you're lucky someone might find it on the front-page of PyPI or the GitHub search or maybe even Hacker News or Reddit. What happens next is on you really. But what does that mean? Before jumping right to the command line and installing your package, those who are interested usually try to find out what problems the project is solving and how it can help them with their own. That's what your README file is for - it's most likely the first thing potential users read, that you control. A good README briefly and concisely explains what your software does, how it can be installed and what API it exposes. You also want to provide information on the requirements, the license it uses and how the project is managed. Who are you? How to get in touch to report problems and give feedback? Where can I find the Code of Conduct for this project? This talk is for everyone who is interested in working on open source projects and wants to know how documentation can help newcomers and more experienced users use your code and to encourage them to engage in the community

uk: вхід без авторизації


uk: Комп'ютерні науки

description: MSS - Software for planning research aircraft missions [EuroPython 2017 - Talk - 2017-07-12 - Anfiteatro 1] [Rimini, Italy] http://www.geosci-model-dev.net/5/55/2012/ Scientific aircraft research flights have to be planned beforehand. For that it is necessary to have model forecasts of relevant quantities such as meteorological parameters, chemical composition or particle information to guide the aircraft to the location of interest. Typically, many scientific instruments on board those aircrafts used to investigate e.g. the chemical composition of the air in order to get new insights often with the involvement of different science groups. For discussion of the possibilites of the research flights, the Mission Support System (MSS) was developed (http://mss.rtfd.io). This software helps to review a big amount of metereological and model data by viewing the forecasted parameters of interest along possible regions of a proposed flight path. Data and possible flight paths can be displayed on a hoizontal view (map projection) or on a vertical view (along the proposed flight path). Flight paths can be constructed and modified on these views. Exchange through a waypoint table is also possible. The talk gives a brief insight into the MSS software development current state. We are using the OWS interface standard. https://geopython.github.io/OWSLib/ MSS is a client/server application. The QT client interacts with a paste wsgi server. The software is available for all platforms on conda-forge

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Optimization using Flow Networks in NetworkX. [EuroPython 2017 - Talk - 2017-07-13 - Arengo] [Rimini, Italy] Prerequisite: Basic Programming. Goals: Introduction to NetworkX Library Using NetworkX for optimization Techniques using Network Flow. This talk can be divided into three major parts. Introduction to NetworkX Basic Introduction to Network Flow. The solution of (https://en.wikipedia.org/wiki/Max-flow_min-cut_theorem#Project_selection_problem) Project selection problem using Network Flow and NetworkX. 1. Introduction to NetworkX. What is NetwrokX? Creating a graph in NetworkX. Some awesome methods Algorithms available. Using with other libraries like Pandas. 2. Basic Introduction to Network Flow. Origin of Problem: Mincut of soviet union railway network. A quick explanation of Max-Flow and min-cut problem. Max-flow = min-cut How to reduce problems for Network Flow optimization? Model the problem for using NetworkX 3. Solution of Project selection Problem using NetworkX. Problem statement. How can we solve it using max flow / min-cut? Modeling in form of graph. Proof of correctness Representing the graph in NetworkX Finding answer in Network

uk: вхід без авторизації


uk: Комп'ютерні науки

description: AAA Games with Unreal Engine 4 and Python [EuroPython 2017 - Talk - 2017-07-10 - Anfiteatro 2] [Rimini, Italy] Unreal Engine is the game industry leading platform for developing AAA Videogames. Now you can script your gameplay logic, automate your development pipelines or test your productions with Python too. The UnrealEnginePython plugin aims at covering the whole feature set of Unreal Engine 4 and extending it with the huge modules library available in the Python world. The talk will briefly introduce the Unreal Engine platform and its capabilities as well as how it can be programmed without the python plugin (Blueprints and C++). The biggest part of the talk will focus on how the Unreal Engine 4 patterns are mapped to the python plugin, what is already working and what you can expect from future developments. No prior knowledge of game/graphics programming is required, and if you are in the movie or cartoon industry you can get interesting infos to

uk: вхід без авторизації


TCP / IP Animated

uk: Комп'ютерні науки

description: TCP / IP Animated [EuroPython 2017 - Interactive session - 2017-07-10 - PythonAnywhere Room] [Rimini, Italy] This interactive game teaches is the follow-up of the Router Game by Roberto Polli, and teaches various TCP / IP protocols using paper and pen. Participants are divided in teams, simulating exchanges through various protocols (DNS, TCP, IP) Every player has an L3 role: a PC or mobile phone, a Router, a Load Balancer ... and must communicate with the others following the associate specification (eg. a TCP client may buffer frames, a Load Balancer re-encapsulates IP datagram, ... ) The team which is faster in exhanging messages wins

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Simple ETL in python 3.5+ with Bonobo

uk: Комп'ютерні науки

description: Simple ETL in python 3.5+ with Bonobo [EuroPython 2017 - Talk - 2017-07-12 - Arengo] [Rimini, Italy] Simple is better than complex, right? That’s true for data pipelines too. For more than 5 years, I hacked together extract-transform-load (ETL) processes in various different positions (ETL is just a fancy term for «bunch of things that take data somewhere and put it elsewhere, eventually transformed»). I did it as a founder, as a consultant, as a technical co-founder, for some side projects, and now in a big corp (to be continued…). In each case, I felt frustrated with the tools available, and in some serious cases, I had to hack things myself to get the job done. https://www.bonobo-project.org/ Bonobo is the repackaging of my past experiences for python 3.5+, and grasping the basics should not take more than the length of the presentation. Topics outline (subject to small changes) : • INTRO : State of the art / different tools for different needs. • Where does it come from. • Writing a data processor. • Running and monitoring data jobs. • OUTRO : The road ahead. • Q&A Bonobo is the glue you need to tie together regular functions in a transformation graph (think unix pipes). Execution strategies are abstracted so you can focus on the real operations. As a result, you can engineer simple and testable systems, using the same good computer development practices as you use in -insert your favorite field here-. Spoiler : there is no «big data» in this talk

uk: вхід без авторизації


uk: Комп'ютерні науки

description: How Booking.com serves Deep Learning model predictions [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 1] [Rimini, Italy] With so many machine learning frameworks and libraries available, writing a model isn’t a bottleneck anymore while putting your models in production is still a challenge. In this talk, you will learn how we deploy the python deep learning models in production at Booking.com. Topics will include: Deep Learning model training in Docker containers Automated retraining of models Deployment of models using Kubernetes Serving model predictions in containerized environment Optimising serving predictions for latency and throughpu

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uk: Комп'ютерні науки

description: Fast product development using Django Rest Framework. #lessonslearned [EuroPython 2017 - Talk - 2017-07-13 - PyCharm Room] [Rimini, Italy] A year ago we decided to use Django and, specifically, DRF as the cornerstone of our upcoming fast-iterating web platform. In this talk I discuss why and how the architecture has evolved, key decisions made and the lessons we learned along the way. I'll share the techniques we used for iterating quickly and how the technology supported (and shaped) them. In addition some quirks and DRF-specific tricks will be brought along the way. Expect a general talk about how Python (and specifically DRF) can be used as a based for quick product iterations, a discussion regarding how to build and evolve a platform to meet those needs and some DRF-specific tidbits. No previous knowledge is required but experience in web development will make the content more accessible

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uk: Комп'ютерні науки

description: Why you don't need design patterns in Python? [EuroPython 2017 - Talk - 2017-07-11 - PyCharm Room] [Rimini, Italy] Exactly 23 years have passed since release of one of the biggest IT classics - ""Design Patterns: Elements of Reusable Object-Oriented Software"". Contents of the book had considerable influence on dominant programming languages of those days. However, design patterns were not glorified by everyone. Voices of rational critic appeared, pointing out that design patterns are just ways to compensate for missing languages features by tons of clumsy code. If one implements design patterns in Python by the book, they will get code that looks awkward, at best. This talk is to present Python's features that either simplifies or eliminates need for implementing design patterns. Even if you don't know what design patterns are - don't worry. You still may see some new interesting Python's features and their usage

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uk: Комп'ютерні науки

description: "There should be one obvious way to bring python into production [EuroPython 2017 - Talk - 2017-07-11 - PyCharm Room] [Rimini, Italy] The DevOps methodology is reality. That said, many developers have to deal with the process of bringing python applications into production. One aphorism of the Zen of Python states: ""There should be one-- and preferably only one --obvious way to do it."" While for the language itself and code being written with it, this advice is often strictly followed for good reasons. For the process of bringing python into production it is not. In the first part of this talk I will give an overview of the different steps in the delivery pipeline, from packaging to deployment and the various possible implementations for each of those steps that emerged over the last years. We will discuss docker, pex, wheels, debs, and tars. We will learn why fixed environments are crucial and why pip is not yet what it should be. We learn about the different runtime environments, from bare metal servers over ephemeral container clusters to “serverless” and what they impose on the deployment process and scratch on the surface of configuration management using tools like Ansible and orchestration frameworks like Kubernetes. We will also explore the world beyond python by looking into other languages ecosystems and learn how they addressed and solved this issue. After we understood the requirements and realized that there is not ""one obvious way to do it"", this talk is a call to action: Let us define and build the “one obvious way” of how we want to bring python into production!

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uk: Комп'ютерні науки

description: Python Profiling with Intel® VTune™ Amplifier [EuroPython 2017 - Talk - 2017-07-10 - PythonAnywhere Room] [Rimini, Italy] Python has grown in both significance and popularity in the last years, especially in the field of high performance computing and machine learning. When it comes to performance, there are numerous ways of profiling and measuring code performance—with each analysis tool having its own strengths and weaknesses. In this talk, we will introduce a rich GUI application (Intel® VTune™ Amplifier) which can be used to analyze the runtime performance of one’s Python application, and fully understand where the performance bottlenecks are in one’s code. With this application, one may also analyze the call-stacks and get quick visual clues where one’s Python application is spending time or wasting CPU cycles

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Video Abstraction (07.07.2011)

uk: Комп'ютерні науки

description: In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees

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name: Technische Universität Braunschweig, Institut für Informationssysteme

addressCountry: DE

addressLocality: Braunschweig

addressRegion: Lower Saxony


uk: Комп'ютерні науки

description: Scientific computing using Cython: Best of both Worlds! [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 2] [Rimini, Italy] Cython is not only an excellent and widely used tool to speed up computational Python code, it’s also a very smart way to talk to native code and libraries. The Cython compiler translates Python code to C or C++ code, and supports static type annotations to allow direct use of C/C++ data types and functions. You get the best of both worlds while working with Cython: Python like syntax with blazing fast C speed. This talk/tutorial by a Python/Cython developer introduces Cython programming language and leads the participants all the way from their first Python extension to an efficient integration with native C. Topics covered will be: 1. Using the Cython compiler to build a native extension module 2. Cython development from Jupyter notebook 3. Mixing Python with static C types in the Cython language 4. Calling into native code from Cython code (Brief introduction) 5. Wrap up: A brief case study Cyvlfeat: A Cython/Python wrapper for Computer Vision library, VLFeat. Participants are expected to have a good understanding of the Python language, some basic knowledge about C or C++. No deep C programming knowledge is required, nor is any prior knowledge needed about writing extension modules for the CPython runtime

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uk: Комп'ютерні науки

description: Fixture factories for faster end-to-end tests [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] When developing and maintaining many different services, unit testing is not enough to make sure your code works in production. By now, many teams doing SOA (service-oriented architectures) have a set of end-to-end tests that cover critical workflows to make sure these work. For these tests, all of the utilized services need to have the proper test fixture data in their datastores. This often leads to developers having to deal with raw datastore data (like JSON or SQL) for these tests, making the authoring of those tests very slow, tedious, and error-prone. This talk is going to discuss several approaches we tried at Yelp to generating these fixture data in a quicker, developer-friendly and more correct way. The main part of the talk will be a deep-dive into what fixture factories are, how to implement them and how to integrate them with pytest, the leading Python testing framework. I'll show you several other benefits this approach has over writing raw fixture data and how this leads to more maintainable and easier to adapt code. We'll also explore how you can then run your tests in parallel, cutting down runtime drastically

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keywords: TDD Refactoring Tests BDD Selenium CI

uk: Комп'ютерні науки

description: Django: From a nightmare to a dream with Best Practices. [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 1] [Rimini, Italy] With the adventure in the source code of the site of EuroPython, we have detected that the code was difficult to maintain. With the members of the Web team, we have started to refactor the code with the best practices and good concept. Also, we will see some awesome libraries for the Django project. In brief, you have a legacy code and you want to improve it with best practices, then this talk is for you. You don't want to break it but you don't want to change it because you can break it at all times, we will show you how to improve an existing code with some tools and best practices, and your code can be deploy on Friday evening with no risk. From a nightmare to a dream.

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uk: Комп'ютерні науки

description: Optimizing queries for not so big data in PostgreSQL [EuroPython 2017 - Talk - 2017-07-13 - Arengo] [Rimini, Italy] Hotjar’s user recordings count above 400 million, with supporting tables containing 4.5 billion records. This 5TB data fits nicely into Postgres and doesn’t quite merit the full big data suite of tools. However, at the rate of 1000 recordings per minute, and overall request rate of 750K per minute, the penalty of inefficient queries and updates can quickly cause nasty performance spikes if not thought out well. This talk is about the challenges we faced at the lower end of big data: the good decisions which helped keep our application running and other lessons we had to learn the hard way Considerations for Database Design Design entities for the domain Balance normalization with performance Sharding later has big migration costs, consider designing for this early Speak to the database from your Web Application Why use ORMs and at which level of abstraction? Stored Procedures are fast, should we have more of those? Bringing data closer to the application Materialize Views Defer aggregations Application Level Caching Handling Operational Troubles Explain(analyze, buffers) is your friend Detect and manage Index Bloat Reduce Deadlocks Reducing Impact of Background Maintenance Jobs Keep impact on database low with cursors and streaming Plan data retention policies early, so cleaning can be an ongoing proces

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OpenAPI development with Python

uk: Комп'ютерні науки

description: OpenAPI development with Python [EuroPython 2017 - Talk - 2017-07-11 - PythonAnywhere Room] [Rimini, Italy] Goal After this session, audiences will get - Basic knowledge of OpenAPI (Swagger) - Ecosystem of OpenAPI(Swagger) and tools related to Python and knowledge to make use of them in their own projects Audience (1) This talk is for: - Developers who are creating or using API services such as web applications, mobile applications and all other kinds of applications (2) Audience will be expected to have (prerequisite): - Basic knowledge of development with API (REST and others) - Basic knowledge of Python Outline Introduction (3min) Agenda of this talk Myself introduction OpenAPI(Swagger) introduction and basics (8min) What is OpenAPI and what is the relation with Swagger? (5min) Comparison with other frameworks for APIs (3min) api blueprint, json schema and .. OpenAPI ecosystem and tools (8min) OpenAPI(Swagger) core tools (4min) Swagger editor, Swagger codegen and Swagger UI OpenAPI tools related to Python (4min) Some tools for python api development with OpenAPI Actual case study with OpenAPI and Python (8min) Introduce our projects with OpenAPI, Python and other program (like Angular, Typescript and so on) and explain how swagger is well working in our company Recap and Conclusion (2min

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uk: Комп'ютерні науки

description: Jupyter notebooks for teaching and learning [EuroPython 2017 - Talk - 2017-07-10 - Anfiteatro 1] [Rimini, Italy] The Jupyter Notebook (formerly IPython Notebook) has been used to support learning in different scenarios, including taught courses, self-directed learning and reference material such as software documentation. People have used it to learn how to program, and to learn about diverse subjects where computer code is important to human understanding. The aim of this talk is to dive into where and how notebooks can be used most effectively for education. I will first describe notebook-based learning material created by a variety of people for different purposes, deliberately taking a broad definition of ‘education’, along with my own experiences using notebooks to teach Software Carpentry sessions and conference tutorials. I’ll pull out both strengths and limitations of notebooks as an educational tool to explore how they can be used most effectively. In the second part of the talk, I’ll talk about several extra software tools which can make the notebook more valuable in educational settings, including Jupyterhub, with which a teacher can provide notebook servers for a group of students, nbgrader, which allows notebooks to be used as assignments, and cite2c, which can insert academic citations into notebooks. I’ll also touch on commercial offerings integrating the notebook, such as SageMathCloud

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Front-end testing with Python

uk: Комп'ютерні науки

description: Front-end testing with Python [EuroPython 2017 - Talk - 2017-07-10 - PyCharm Room] [Rimini, Italy] Web-developers use Python to develop web-applications, serving HTML or JSON content to user's browsers which render it. We test our applications to check that they serve content right and that they respond to HTTP requests in an expected way. However, this is not all that happens in a modern web application. There are lots of moving parts which are executed not on the server, but directly in user's browser, and they need to be tested too. The best way to check that our app works well in a browser is to test it in a browser, and Python gives you tools to do that conveniently. With a Selenium tool and Python package, you can control the browsers, making them open web pages and interacting with them. I will show how to install Selenium and needed drivers, and tell about best practices for writing Selenium tests, such as Page Object pattern Put browser interactions in the page object, not the test Put assertions in the test, not the page object Never use time.sleep() Always make pages wait for actions to complete Wait for JavaScript to load To test the visual look of your application there is a Needle tool, which lets you compare screenshots with baseline set, and highlight the differences. I will talk about how to incorporate that functionality into your tests. As browser testing can get quite slow, I will also show how to set up running them concurrently

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uk: Комп'ютерні науки

description: Automating Instagram with Python and Selenium [EuroPython 2017 - Talk - 2017-07-12 - Anfiteatro 1] [Rimini, Italy] In this talk about using Selenium and Python for social media automation you will get some insights into growing your own open source projects into tools that really get used and maintained by a community. You will get an idea of what pitfalls you have to face when working with something as fast changing as Instagram and how to best tackle this. You'll learn what the ""Page Object"" design pattern is, what it is useful for and why we use it. Of course we will also talk about OpenSource and why it's important. A lot of the talk will be based on my article on InstaPy published at the freeCodeCamp publication on Medium: https://medium.freecodecamp.com/my-open-source-instagram-bot-got-me-2-500-real-followers-for-5-in-server-costs-e40491358340 So, who is this talk suited for? If you're just starting out with python, are interested in automation or simply like to see a fun and interesting little open source project, I'd love to see you at my talk and hear your ideas and opinions about it

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The Different Roads We Take

uk: Комп'ютерні науки

description: The Different Roads We Take [EuroPython 2017 - Keynote - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] We've all taken different routes to get to where we are today, and we're not all currently on the same road going the same place. Tracy Osborn will talk about the idea of the ""Python engineer,"" her (long and full of bumps and potholes) journey to learning and teaching Python, and the harmful myths about learning programming and the paths available when you do so

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Pandas - not just for data scientists

uk: Комп'ютерні науки

description: Pandas - not just for data scientists [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 1] [Rimini, Italy] This is not a tutorial. It's an attempt to expose non data scientists experienced pythonistas to the powerful pandas library. Most of python developers don't use pandas (either because they never heard of it, felt that it's a too steep learning curve or never thought that it will be useful for them). I intend to talk about python performance limitations and show how pandas can be used to overcome some of these limitations. The talk will be accompanied by a live Jupiter Notebook session that will demonstrate a typical use of pandas

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Pythonic JavaScript for Web Developers

uk: Комп'ютерні науки

description: Pythonic JavaScript for Web Developers [EuroPython 2017 - Talk - 2017-07-12 - PythonAnywhere Room] [Rimini, Italy] Having a basic knowledge of JavaScript is obligatory for every Web developer in todays world. Most of us are familiar with the pain of having to write some simple hide/show logic and ajax queries with jQuery on a page that is mostly rendered server-side. Nowadays, however, there are lots of great JavaScript frameworks and build tools that not only make these things very easy to do The Right Way, but also make the whole process quite painless. I spent past year in a small startup writing only front-end code and evaluating different tools and best practises. Bringing these tools to old Django projects has been eye-opening. There are practically no documentation or code examples on how to integrate these things to Python backend projects, so it’s very hard to get started from scratch. In this talk we’ll go trough some of the tools, examples on how to get started, and also some coding guidelines on how to make JavaScript look and feel more sane and Pythonic. The current state of JavaScript frameworks is notoriously bad; there are more of them than blog engines written with Django. We’ll focus on Vue.js (https://vuejs.org) which is a lightweight, very easy to get started and yet powerful tool. We’ll also take a look at new end-to-end browser test tools and modern build tools that enable us to take full advantage of the huge NPM package universe (Cheeseshop for JS) and write modern ES2015/ES2016 JavaScript that has more powerful and cleaner syntax

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Green threads in Python

uk: Комп'ютерні науки

description: Green threads in Python [EuroPython 2017 - Talk - 2017-07-12 - PyCharm Room] [Rimini, Italy] This talk is for general public with problems to scale. The good background for this talk is people with minimal knowledge about threads. After this talk I expect that the audience can be enable to understand multiprocessing, when use and which tools and methods are appropriate for each case. Understanding threads It's the first moment on the talk. Here I'll speak about the thread theory and what happen when we use threads on the OS Threads and multiprocess API I'll give simple examples about what python give us to use threads and multiprocessing perhaps with two APIs https://docs.python.org/3/library/threading.html https://docs.python.org/3.5/library/multiprocessing.html Understanding Green threads Here I'll speak about green threads theory and what happen when we use green threads on our OS Green thread Patterns and tools Here I'll show few examples with keep alive patterns, discuss about a few tools and show gevent and asyncio examples Parallelism x asynchronism I'll show the diference between parallelism and asynchronism discussing about the two theories Why, when and how It's is the last talk moment and I'll explain the how complex is work with multiprocess showing cases like queues consume and APIs consum

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Feeding a real-time user interface

uk: Комп'ютерні науки

description: Feeding a real-time user interface [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 2] [Rimini, Italy] Imagine you have some streaming computations running on a server. Client programs subscribe to real-time updates, so that they may visualise the computations for end users. How do you share this constantly changing server state with all connected clients? Sending an entire snapshot after each change is very inefficient, so you must implement some sort of incremental updates – diffs. But how do you generate these diffs on the server? And how do you represent them so the clients know how to apply them to update their own state? We have been working on these problems for a long time while building a stock trading platform in Python. I'd like to show you a couple of open source libraries that we developed for this purpose, and share our experience with tracking state and propagating it to user interfaces running in other processes

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uk: Комп'ютерні науки

description: Lessons learnt building a medical chatbot in Python [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 2] [Rimini, Italy] ""To put an accessible and affordable health service in the hands of every person on earth."" Thats our mission at babylon. Leveraging the powers of AI, python and micro services we took a step towards that vision by building a medical chatbot that we shipped in November last year. In this talk I would like to share with you all the things we learnt in the process. This talk is our story. Its a story that starts with an idea and meanders through the dark and dangerous land of things like Graph databases, machine learning and async programming in python. The story is far from over, but we have come to a point where we would like to reflect and share with the community all that we have learnt. More specifically I will cover: - Architecture decisions we made and why we made them - lessons learnt doing async in python at scale - testing chatbots - clinical governance and safety (literally 2 sentences, I promise) - The drawbacks of REST - Why I am glad we did most of it in Python And then of course some time for questions at the end :

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Skynet your Infrastructure with QUADS

uk: Комп'ютерні науки

description: Skynet your Infrastructure with QUADS [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 1] [Rimini, Italy] The very small 2-person DevOps team within Red Hat Performance/Scale Engineering has developed a set of Open Source Python-based systems and network automation provisioning tools designed to end-to-end automate the provisioning of large-scale systems and network switches using tools like Foreman, Ansible, and other Open Source bits. QUADS – or “quick and dirty scheduler” allows a normally overburdened DevOps warrior to fully automate large swaths of systems and network devices based on a schedule, even set systems provisioning to fire off in the future so they can focus on important things like Netflix and popcorn or not reading your emails while your datacenter burns in an inferno of rapid, automated skynet provisioning. QUADS will also auto-generate up-to-date infrastructure documentation, track scheduling, systems assignments and more. In this talk we’ll show you how we’re using QUADS (backed by Foreman) to empower rapid, meaningful performance and scale testing of Red Hat products and technologies. While QUADS is a new project and under constant development, the design approach to handling large-scale systems provisioning as well as the current codebase is consumable for others interested in improving the efficiency and level of automation within their infrastructure

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Introduction to TensorFlow

uk: Комп'ютерні науки

description: Introduction to TensorFlow [EuroPython 2017 - Talk - 2017-07-14 - Anfiteatro 1] [Rimini, Italy] Deep learning is at its peak, with scholars and startups releasing new amazing applications every other week, and TensorFlow is the main tool to work with it. However, Tensorflow it's not an easy-access library for beginners in the field. In this talk, we will cover the explanation of core concepts of deep learning and TensorFlow totally from scratch, using simple examples and friendly visualizations. The talk will go through the next topics: • Why deep learning and what is it? • The main tool for deep learning: TensorFlow • Installation of TensorFlow • Core concepts of TensorFlow: Graph and Session • Hello world! • Step by step example: learning how to sum • Core concepts of Deep Learning: Neural network • Core concepts of Deep Learning: Loss function and Gradient descent By the end of this talk, the hope is that you will have gained the basic concepts involving deep learning and that you could build and run your own neural networks using TensorFlow

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Understanding Celery & CeleryBeat

uk: Комп'ютерні науки

description: Understanding Celery & CeleryBeat [EuroPython 2017 - Talk - 2017-07-10 - Arengo] [Rimini, Italy] Celery is a distributed task queue for Python. Although it is most popular in the web development ecosystem, it has a wide area of usage from system management to IoT devices. With Celery, transforming a function into a task is quite easy and can add great performance & usability to the applications that we build. This talk aims to give attendants a general overview on Celery and its uses. We will walk through the core Celery architecture by introducing key components with the help of various real-world examples. This will also lead to an understanding of the task queue systems in general. Attendants will also gain knowledge about Celerybeat; a tool that focuses on scheduling tasks. We will be looking for the answers to the following questions: What is a distributed task queue? What are the main elements of Celery? When should we use Celery tasks? How do we use Celery Beat? Attendants should have a basic knowledge of Python, and a minor development experience

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uk: Комп'ютерні науки

description: How CPython parser works, and how to make it work better [EuroPython 2017 - Talk - 2017-07-12 - PythonAnywhere Room] [Rimini, Italy] The part of CPython core that parses the Python source code is some very old and convoluted code: the time has proven its reliability, but few CPython hackers understand (or care) how it works, or even what exactly it does. There is, however, a good reason to care: for short-running scripts, the performance of CPython may easily be dominated by that of parsing the source code. The talk will describe the two parsers that are involved, it will explain how these two parsers build two different kinds of syntax trees, and then show how the structure of one of the trees can be amended to reduce its memory footprint threefold, with only minor changes necessary in its consumers. It will also suggest other, more invasive improvements, which can yield even better savings. The talk will assume fluency in C and a basic acquaintance with CPython core internals, and will give the attendees an introduction into hacking the parser, guiding their way through to the very tangible end result of reducing Python overall memory consumption by up to 30%, measured at standard micro-benchmarks

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uk: Комп'ютерні науки

description: Pythonist view on Microservices & Containerization [EuroPython 2017 - Talk - 2017-07-14 - Arengo] [Rimini, Italy] Microservices architecture and containerization are words thrown around when we talk about designing systems that are loosely coupled, although it may sound like buzz words but these key concepts play a very important part in system as a whole. In this talk, we will cover how microservices can be implemented in python using available open source frameworks and how it can be deployed independently to scale and perform in production environment. I'll also share several use-cases where it is worth planning and developing system architecture considering microservices/containerization and will also discuss some trade-off of having such architecture. Outline: Overview of microservices Implementing microservices using Python Advantages of microservices over Monolithic / SoA architecture Overview of containerization How to containerize Python based services (Docker) Advantages of microservices/containerization over traditional deployment CI/CD Approach in microservices and containerization Usecases where to use microservices Trade-off of using microservice

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uk: Комп'ютерні науки

description: But how do you know your mock is valid? Verified fakes of web services [EuroPython 2017 - Talk - 2017-07-10 - Arengo] [Rimini, Italy] If your code calls a third party service then you may want to test that your code works but you don't want to call the service in your tests. It may be expensive, slow or impossible to call that service. For example, if you are making a Slack bot, you want to create tests which don't make calls across the network to Slack. One approach is to create a mock of that service. Our tests can now run quickly, cheaply and reliably. But if we copy the service incorrectly, or if the service changes, our tests will pass while our code does not work. Verified fakes solve this problem. You can write tests which confirm that your mock is an accurate representation of the service being mocked. Those tests can be a small subset of your test suite and they can be run periodically, to verify the validity of the many tests which use the mock. This talk will follow the example of VWS-Python, a verified fake for a proprietary web service. It will discuss the practicalities of creating such a fake and it will focus on the trade-offs, tooling and approaches involved. By the end of this talk the audience will understand how to tie together pytest, Travis CI, requests and Responses to create a verified fake. The talk is aimed at people who have an interest in writing correct software. It is assumed that the audience is familiar with basic testing techniques

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uk: Комп'ютерні науки

description: Building a real-time embedded audio sampling application with MicroPython [EuroPython 2017 - Talk - 2017-07-10 - Arengo] [Rimini, Italy] While demonstrating the pyboard to a group of colleagues, a challenge was set to produce a practical demonstration of the device that would provide automatic and continuous voice recording and playback of short spoken phrases similar to that found in a number of talking toys. This talk covers the process of designing and testing the embedded real-time Python solution and includes the architecture, test methodologies and recordings as the stages progressed to the final source code. The talk concludes with a live demonstration of the final application. The solution uses MicroPython (an embedded implementation of Python 3), the pyboard and its AMP Audio skin. MicroPython is a lean implementation of Python 3 that is optimised to run in a very small footprint on micro-controllers and in constrained environments. It was created by the Australian programmer and physicist Damien George, after a successful Kickstarter backed campaign in 2013. The pyboard is the original reference hardware created to host MicroPython. It is a compact low-power board based on an ARM processor with a heap of approximately 100kBytes that can run at 168MHz. It has sufficient hardware services and real-time capabilities to control all kinds of electronic projects. The AMP Audio skin is a small additional module that attaches to the pyboard that adds a small power amplifier, speaker and a microphone with a pre-amp

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uk: Комп'ютерні науки

description: Get over the boundaries between client and server in web app development [EuroPython 2017 - Talk - 2017-07-11 - Anfiteatro 2] [Rimini, Italy] The practice in the development of advanced web applications is to break it into two main areas where the effort on the server, with the typical WSGI environments, focuses on data access configuration whereas the application logic is delegated mostly to the client through the use of JavaScript frameworks. The reason for this separation into two roles lies in the need to use JavaScript on the browser and the fact that the communication channel (HTTP) and the way in which the application state is handled on the server side is still that thought to serve full web pages. In this talk I'll show a framework and an application where the line between the two worlds is blurred and where it is possible to think of the application in terms of unity, with the two components that cooperate equally and communicate without thinking in terms of URLs or HTTP verbs . This is made possible by the asynchronous/reactive management of the processing from the database driver to the user interaction, the use of websocket for communication and the use of Python (optional) for the development of the client part. Some technologies used are: PostgreSQL, the ""asyncpg"" driver, the ""aiohttp"" web server, Crossbar (http://crossbar.io) for the websocket comms, pkg ""metapensiero.sphinx.patchdb"" for schema upgrades, the package ""metapensiero.reactive"" and ""metapensiero.signal"" for the management of reactive event streams,the package ""Javascripthon"" for Py3 to ES6 JS transpiling

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Django Rest Framework, one year after: tips, tools, tricks and pitfalls. [EuroPython 2017 - Talk - 2017-07-10 - Anfiteatro 2] [Rimini, Italy] One year ago we started OnTruck. Our CTO had previously had some success with DRF so after discussing it, we decided to give it a try. After a year building a quickly iterating startup on top of Django Rest Framework, we have reflected a lot on it. Even now, with a full year of development on top of it, we still periodically discuss going forward or dropping it. For the time being it still wins. So we think we have learned a lot about it. This talk is a walkthrough of the discoveries & learnings we have made at OnTruck during that year. We will cover different aspects of the framework, what advantages it has given us and also what tradeoffs it has forced. We will discuss both how it relates to Django on the inside and how it relates with other systems outside. We will approach it both from a purely user perspective and also discuss some of its internals and the way we tweaked them to make it work for us. We will cover the pitfalls we have both avoided and fallen into. The tricks that have helped us keep our speed and sanity. The tools we have both used and discarded on the way. And finally the tips we would give ourselves if we were starting all over again after this year

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Fast Python! Coding competitions with CPython and PyPy [EuroPython 2017 - Talk - 2017-07-12 - Anfiteatro 2] [Rimini, Italy] In a coding competition you want to solve problems ""fast""... why would you choose a ""slow"" interpreted language like Python? Because the ""slow"" Python wins competitions more often that most people think. We will show how coding competitions work, what are the resources and constraints that competitors need to take into account, and we will find out that, like very often in real life, the actual processing time is only a small term in the complex equation that describe a competition. The ""faster"" PyPy may help in gaining raw speed, but that is not the real advantage it gives you in a competition

uk: вхід без авторизації


uk: Комп'ютерні науки

description: Kajiki, the fast and validated template engine you were looking for [EuroPython 2017 - Talk - 2017-07-10 - PyCharm Room] [Rimini, Italy] Being dissatisfied with some of the constraints and complex usage of Genshi, the TurboGears2 team started working on an alternative that could solve Genshi speed issues, complex inheritance system and be backward compatible with genshi templates. This is a talk about what lead to the creation of the Kajiki template engine and what's particular about it. The talk will cover: - Comparison on the major template engines available in python to showcase what's special in Kajiki (validated, xml based) what was special in Genshi (also lazy evaluated) - What's a validated template engine and why it's good to have one. - How Kajiki works, showcase kajiki syntax, it's performances and how to use it in any python project. - Why Kajiki is fast, code generation applied and how to write a code generation template engine like Kajiki and Jinja2 (showcase a simple 50 lines of code template engine that uses code generation)

uk: вхід без авторизації


14. Vorlesung vom 12.06.2013

de: Physik, Astronomie

fi: fysiikka, tähtitiede

uk: Фізика, Астрономія

en: Physics, Astronomy

fr: Physique, Astronomie

da: Fysik, Astronomi

es: Física, Astronomía

nl: Natuurkunde, Astronomie

de: Mathematik, Naturwissenschaften

uk: Математика, природничі науки

en: Mathematics, Natural Sciences

@language: de

uk: вхід без авторизації

Leibniz Universität Hannover (LUH)

addressLocality: Hanover


8. Vorlesung vom 08.05.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


18. Vorlesung vom 03.07.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


15. Vorlesung vom 19.06.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


17. Vorlesung vom 26.06.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


19. Vorlesung vom 04.07.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


16. Vorlesung vom 20.06.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


4. Vorlesung vom 17.04.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


9. Vorlesung vom 15.05.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


10. Vorlesung vom 16.05.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


2. Vorlesung vom 04.04.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


11. Vorlesung vom 29.05.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


13. Vorlesung vom 06.06.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


12. Vorlesung vom 05.06.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


5. Vorlesung vom 18.04.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


3. Vorlesung vom 10.04.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


1. Vorlesung vom 03.04.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


6. Vorlesung vom 24.04.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


7. Vorlesung vom 02.05.2013

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Комп'ютерні науки

description: In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features - Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees

uk: вхід без авторизації

name: Texture Features, Low-Level Texture Features, Tamura Measure, Random Field Models, Transform Domain Features (21.04.2011)


uk: Комп'ютерні науки

de: Rechts-, Wirtschafts- und Sozialwissenschaften

uk: Право, економіка та суспільні науки

en: Law, Economics and Social Sciences

de: Wirtschaftswissenschaften

uk: Бізнес та економіка

en: Business and Economics

uk: вхід без авторизації

name: Joint ECB/MIT Conference: Keynote speech - 13 Mar 2017

European Central Bank (ECB)

addressLocality: Frankfurt am Main

addressRegion: Hesse


uk: Право, економіка та суспільні науки

uk: Комп'ютерні науки

uk: вхід без авторизації

name: Joint ECB/MIT Conference: Welcome Address - 13 Mar 2017


uk: Право, економіка та суспільні науки

uk: Бізнес та економіка

uk: Комп'ютерні науки

uk: вхід без авторизації

name: Joint ECB/MIT Conference: Keynote Speech - 14 Mar 2017


uk: Право, економіка та суспільні науки

uk: Бізнес та економіка

uk: Комп'ютерні науки

uk: вхід без авторизації

name: Joint ECB/MIT Conference: Opening - 13 Mar 2017


uk: Комп'ютерні науки

description: In this course, we examine the aspects regarding building multimedia database systems and give an insight into the used techniques. The course deals with content-specific retrieval of multimedia data. Basic issue is the efficient storage and subsequent retrieval of multimedia documents. The general structure of the course is: - Basic characteristics of multimedia databases - Evaluation of retrieval effectiveness, Precision-Recall Analysis - Semantic content of image-content search - Image representation, low-level and high-level features - Texture features, random-field models - Audio formats, sampling, metadata - Thematic search within music tracks - Query formulation in music databases - Media representation for video - Frame / Shot Detection, Event Detection - Video segmentation and video summarization - Video Indexing, MPEG-7 - Extraction of low-and high-level features -Integration of features and efficient similarity comparison - Indexing over inverted file index, indexing Gemini, R *- trees

uk: вхід без авторизації

name: Hidden Markov Model, Video Retrieval (09.06.11)


PS Image Rendering Tutorial - 01

keywords:
Photoshop
Adobe
Tutorial
Architektur
Visualisierung
Rendering
Perspektive
Darstellung
Bildbearbeitung
dMA
digitale Methoden

de: Architektur, Innenarchitektur

uk: Архітектура, дизайн інтер'єру

en: Architecture, Interior Architecture

uk: вхід без авторизації

name: Leibniz Universität Hannover (LUH), Abteilung für Digitale Methoden in der Architektur


PS Image Rendering Tutorial - 02

keywords:
Adobe
Photoshop
Architektur
digitale Methoden
Rendering
Perspektive
Bildbearbeitung

uk: Архітектура, дизайн інтер'єру

uk: вхід без авторизації


PS Image Rendering Tutorial - 03

uk: Архітектура, дизайн інтер'єру

uk: вхід без авторизації


PS Image Rendering Tutorial - 04

uk: Архітектура, дизайн інтер'єру

uk: вхід без авторизації


PS Image Rendering Tutorial - 05

uk: Архітектура, дизайн інтер'єру

uk: вхід без авторизації


PS Image Rendering Tutorial - 06

uk: Архітектура, дизайн інтер'єру

uk: вхід без авторизації


PS Image Rendering Tutorial - 07

uk: Архітектура, дизайн інтер'єру

uk: вхід без авторизації


Neues Bauen mit Carbonbeton

name: Unternehmen Region - Die BMBF-Innovationsinitiative Neuer Länder

keywords:
BMBF Innovationsinitiativen Neue Länder
Unternehmen Region

uk: Архітектура, дизайн інтер'єру

description: Das bundesweite „Zwanzig20“-Konsortium „C3“ entwickelt leichten, flexiblen und nachhaltigen Carbonbeton. Er kann für Neubauten sowie für Sanierungen – zum Beispiel von Brücken – eingesetzt werden und soll in Zukunft den schweren, rostanfälligen Stahlbeton ersetzen.

uk: вхід без авторизації

name: Bundesministerium für Bildung und Forschung (BMBF)

addressLocality: Bonn

addressRegion: North Rhine-Westphalia


Bedingte Wahrscheinlichkeit

cs: Matematika

de: Mathematik

fi: matematiikka

uk: Математика

en: Mathematics

fr: Mathématiques

da: Matematik

es: Matemáticas

nl: Wiskunde

uk: Математика, природничі науки

uk: вхід без авторизації

Technische Universität Darmstadt

addressLocality: Darmstadt


uk: Математика, природничі науки

uk: вхід без авторизації

name: Einführung in die Stochastik: Beispiele (Fortsetzung)


Definition des Maßintegrals

uk: Математика, природничі науки

uk: вхід без авторизації


Der Begriff der Wahrscheinlichkeit

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

uk: вхід без авторизації

name: Der Begriff des Wahrscheinlichkeitsraumes


Eigenschaften von Punktschätzverfahren

uk: Математика, природничі науки

uk: вхід без авторизації


Einführung in die Stochastik

uk: Математика, природничі науки

uk: вхід без авторизації


Gesetz der großen Zahlen

uk: Математика, природничі науки

uk: вхід без авторизації


Histogramme, Dichteschätzung

uk: Математика, природничі науки

uk: вхід без авторизації


Kombinatorik und Beispiele

uk: Математика, природничі науки

uk: вхід без авторизації


Lineare Regression

uk: Математика, природничі науки

uk: вхід без авторизації


Linearität des Integrals

uk: Математика, природничі науки

uk: вхід без авторизації


Maximum-Likelihood-Verfahren

uk: Математика, природничі науки

uk: вхід без авторизації


Rechenregeln für Varianzen

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

uk: вхід без авторизації

name: Statistische Maßzahl, Einführung in die Regressionsschätzung


Einführung in die Stochastik: Studien

uk: Математика, природничі науки

uk: вхід без авторизації


Einführung in die Stochastik: Varianz

uk: Математика, природничі науки

uk: вхід без авторизації


Wahrscheinlichkeitsräume mit Dichte

uk: Математика, природничі науки

uk: вхід без авторизації


Zentraler Grenzwertsatz

uk: Математика, природничі науки

uk: вхід без авторизації


Zufallsvariablen

uk: Математика, природничі науки

uk: вхід без авторизації


ZV und Unabhängigkeiten

uk: Математика, природничі науки

uk: вхід без авторизації


Cauchy-Riemann equation

uk: Математика, природничі науки

uk: вхід без авторизації


Cauchy's integral formula

uk: Математика, природничі науки

uk: вхід без авторизації


Complex line integrals

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

uk: вхід без авторизації

name: Complex numbers and holomorphic functions


Fourier series

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

uk: вхід без авторизації

name: Identity theorem for holomorphic functions, Liouville's theorem


uk: Математика, природничі науки

uk: вхід без авторизації

name: Irrotational fields on simply connected domains


Isolated singularities

uk: Математика, природничі науки

uk: вхід без авторизації


Laurent series

uk: Математика, природничі науки

uk: вхід без авторизації


Line integrals

uk: Математика, природничі науки

uk: вхід без авторизації


Residue Theorem and improper integrals

uk: Математика, природничі науки

uk: вхід без авторизації


Smoothness and analyticity

uk: Математика, природничі науки

uk: вхід без авторизації


Vector fields with potential on domains

uk: Математика, природничі науки

uk: вхід без авторизації


Winding Number and Residue Theorem

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Математика, природничі науки

uk: вхід без авторизації

name: Fundamental theorem of algebra, mapping properties of holomorphic functions


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Einführung in Net Centric Systems: (1) Introduction


(2) Graph Theory

uk: Комп'ютерні науки

uk: вхід без авторизації


(3) Routing I

uk: Комп'ютерні науки

uk: вхід без авторизації


(3) Routing II

uk: Комп'ютерні науки

uk: вхід без авторизації


(4) Distributed Algorithms I

uk: Комп'ютерні науки

uk: вхід без авторизації


(4) Distributed Algorithms II, Teil 1

uk: Комп'ютерні науки

uk: вхід без авторизації


(4) Distributed Algorithms II, Teil 2

uk: Комп'ютерні науки

uk: вхід без авторизації


(5) Queuing Theory

uk: Комп'ютерні науки

uk: вхід без авторизації


(6) Distributed Programming, Teil 1

uk: Комп'ютерні науки

uk: вхід без авторизації


(6) Distributed Programming, Teil 2

uk: Комп'ютерні науки

uk: вхід без авторизації


(7) Quality of Service and Scheduling

uk: Комп'ютерні науки

uk: вхід без авторизації


(8) Compression

uk: Комп'ютерні науки

uk: вхід без авторизації


(9) Web Engineering

uk: Комп'ютерні науки

uk: вхід без авторизації


0. Vorbemerkung

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


1.1 Mathematische Vorbemerkung

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


1.2 Messungen und Einheiten

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: 1.3 Messunsicherheit und Fehlerfortpflanzung


2.4 Materie im elektrischen Feld

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


2.0 Vorbemerkung

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


2.1 Elektrische Ladungen

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


2.2 Elektrisches Feld

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: 2.3 Elektrisches Potential und Energie des elektrischen Felds


2.5 Elektrischer Strom

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


2.6 Magnetfelder

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


2.6 Magnetfelder (Fortsetzung)

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


2.7 Magnetische Induktion

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


2.8 Maxwell-Gleichungen

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


3.1 Schwingungen und Wellen

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: 3.1 Schwingungen und Wellen (Fortsetzung)


3.2 Elektromagnetische Strahlung

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


3.3 Lichtausbreitung

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


3.3 Lichtausbreitung (Fortsetzung I)

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


3.3 Lichtausbreitung (Fortsetzung II)

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: 3.4 Abbildungen und optische Instrumente


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: 3.4 Abbildungen und optische Instrumente (Fortsetzung)


3.5 Polarisation

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


4.1 Struktur der Materie

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


4.1 Struktur der Materie (Nachtrag)

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


4.2 Lichtquanten

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


4.3 Laser

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


4.4 Teilchen-Welle-Dualismus

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


4.5 Atomkerne und radioaktive Strahlung

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: 4.6 Wechselwirkung ionisierender Strahlung


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: 4.6 Wechselwirkung ionisierender Strahlung (Fortsetzung)


4.7 Kernreaktionen

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Klausurvorbereitung

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: Nachtrag und Zusammenfassung Kapitel 2.0 - 2.3


uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації

name: Organisatorisches u. Zusammenfassung Kapitel 1


Zusammenfassung Kapitel 2.4 - 2.6

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Features introduction, Color features and color histograms, Matching of color histograms (14.04.2011)

name: Technische Universität Braunschweig, Institut für Informationssysteme


Zusammenfassung Kapitel 2.7 - 3.1

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Zusammenfassung Kapitel 3.1 - 3.3

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Zusammenfassung Kapitel 3.3 - 3.4

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: вхід без авторизації

name: Tropfenfeld elektropneumatisch V2: Versuchsserie 3 - Tropfenabriss, Paste 115174: Dispensor Prototyp 2

name: Scientific Precision Instruments (SPI) GmbH


Zusammenfassung Kapitel 4.2

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Zusammenfassung Kapitel 4.3 - 4.4

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Zusammenfassung Kapitel 4.5 - 4.6

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Lecture 01 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 02 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 03 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 04 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 05 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 06 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 07 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: вхід без авторизації

name: Tropfenfeld Paste 115240 elektropneumatisch V3: Versuchsserie 5 - Tropfenfelder, Dauertest 3 Tage / 5h Paste 115240: Dispensor Prototyp 3


Lecture 09 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 10 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 11 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: вхід без авторизації

name: Highspeed elektropneumatisch V3: Versuchsserie 4 - Tropfenabriss, Paste 115174: Dispensor Prototyp 3


Lecture 13 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 14 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


Lecture 15 Post-Quantum Cryptography

uk: Комп'ютерні науки

uk: вхід без авторизації


description: In der Ringvorlesung "Neue Sicherheitskultur für die Industrie 4.0" geben Experten und Expertinnen aus den Fachrichtungen Maschinenbau, Informatik, Recht, Politik und Wirtschaft einen Einblick in das zur Zeit heiß diskutierte Thema Industrie 4.0. Bei dem Zukunftsprojekt Industrie 4.0 geht es um nicht weniger als eine "vierte industrielle Revolution", konkret aber um eine verstärkte Informatisierung der klassischen Industrien, zum Beispiel der Produktions- und Automatisierungstechnik. Solch ein weit- und tiefgreifender technologischer Wandel hat auch gesellschaftliche, rechtliche und politische Auswirkungen. Weiterhin bekommt der Aspekt Informationssicherheit eine zentrale Bedeutung durch die verstärkte Einbettung, Verteilung und Vernetzung von (oft autonom agierenden) Rechnersystemen. Im Rahmen der Ringvorlesung werden diese Aspekte von Dozenten aus einem breiten Spektrum an Perspektiven anhand einer Reihe von Beispielen betrachtet, mit besonderem Fokus auf die Perspektive der Industrie als Dreh- und Wendepunkt dieses Unterfangens. Abgeschlossen wird die Ringvorlesung durch eine Podiumsdiskussion mit namhaften Vertretern von Politik, Industrie und Forschung.

uk: вхід без авторизації

name: Neue Sicherheitskultur für die Industrie 4.0: Datenschutz


Forensics for ICS Cyber Security - HD

uk: вхід без авторизації


uk: вхід без авторизації

name: Industrie 4.0 - Aus Sicht der industriellen Automation


uk: вхід без авторизації

name: Neue Sicherheitskultur für die Industrie 4.0: Recht


uk: вхід без авторизації

name: Sicherheit aus dem Blickwinkel eines Produktionssystems


uk: вхід без авторизації

name: Caveats in risk assessment in the context of cyber attacks


uk: вхід без авторизації

name: Identität in der Virtualität –„Virtuelle Lebenslagen“ unter „Industrie 4.0“ und Identität als neue ganzheitliche Gestaltungskategorie


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Challenges of the Exascale Era - and how tools can help


MPI Programming for Advanced

uk: Комп'ютерні науки

uk: вхід без авторизації


OpenACC - GPU Programming

uk: Комп'ютерні науки

uk: вхід без авторизації


OpenMP and Performance

uk: Комп'ютерні науки

uk: вхід без авторизації


uk: вхід без авторизації

name: HighSpeed elektropneumatisch V2: Versuchsserie 3 - Tropfenabriss, Paste 115174: Dispensor Prototyp 2


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Predicting Complex Turbulent Combustion Systems Using Large Eddy Simulation and Flamelet-Based Tabulated Chemistry


uk: Комп'ютерні науки

uk: вхід без авторизації

name: The Importance of Software in High-Performance Computing


Tool Totalview

uk: Комп'ютерні науки

uk: вхід без авторизації


Applications and Tools - 2

uk: вхід без авторизації


Applications and Tools

uk: вхід без авторизації


Atmospheric Drag 2

uk: вхід без авторизації


Atmospheric Drag 3

uk: вхід без авторизації


Atmospheric Drag

uk: вхід без авторизації


Basic Concepts Summary

uk: вхід без авторизації


Chapter 2 - Summary

uk: вхід без авторизації


uk: вхід без авторизації

name: Chapter 3 Summary, Angular Momentum, and Moments of Inertia


uk: вхід без авторизації

name: Chapter 4 Summary, Introduction to Perturbations


Earth's Gravity

uk: вхід без авторизації


Elliptical Motion

uk: вхід без авторизації


uk: вхід без авторизації

name: Introduction to Spaceflight Mechanics Winter Term 2015/2016


Lagrange Equations

uk: вхід без авторизації


Molnyia and Tundra

uk: вхід без авторизації


Motion of the Spinning Top

uk: вхід без авторизації


Oblateness Perturbations - 2

uk: вхід без авторизації


Oblateness Perturbations

uk: вхід без авторизації


Orbit Transfer

uk: вхід без авторизації


Orbital Motion - 2

uk: вхід без авторизації


Orbital Motion - 3

uk: вхід без авторизації


Orbital Motion

uk: вхід без авторизації


Orbital Motion - 4

uk: вхід без авторизації


Orbits for Earth Observation

uk: вхід без авторизації


Orbits for Telecommunication 2

uk: вхід без авторизації


Orbits for Telecommunication 3

uk: вхід без авторизації


Orbits for Telecommunication

uk: вхід без авторизації


Orbits in the Geocentric Frame

uk: вхід без авторизації


Osculating Conditions

uk: вхід без авторизації


Other Perturbations

uk: вхід без авторизації


Principal Axes

uk: вхід без авторизації


Reference Frames and Time

uk: вхід без авторизації


Review Chapter 2 - 5

uk: вхід без авторизації


Rocket Propulsion 2

uk: вхід без авторизації


Rocket Propulsion

uk: вхід без авторизації


Shape of the Earth

uk: вхід без авторизації


Spinning Satellites

uk: вхід без авторизації


Summary Chap 6+

uk: вхід без авторизації


The Kepler Problem - 2

uk: вхід без авторизації


The Kepler Problem

uk: вхід без авторизації


The Two-Body Problem

uk: вхід без авторизації


Transfer Optimality

uk: вхід без авторизації


uk: вхід без авторизації

name: Diffusion; Laminare Strömung; Propulsion


Diffusionsgleichung

uk: вхід без авторизації


Biofluidmechanik: Einführung

uk: вхід без авторизації


Elektroosmose

uk: вхід без авторизації


Elektroosmose II

uk: вхід без авторизації


Energetisch optimale Bewegung

uk: вхід без авторизації


Biofluidmechanik: Energiebilanz

uk: вхід без авторизації


Peristaltik

uk: вхід без авторизації


Peristaltik II

uk: вхід без авторизації


Potentialströmung

uk: вхід без авторизації


uk: Комп'ютерні науки

uk: вхід без авторизації

name: Audio Low level Features, Difference Limen, Pitch Recognition (19.05.2011)

name: Technische Universität Braunschweig, Institut für Informationssysteme


Virtuelle Masse; Potentialtheorie

uk: вхід без авторизації


Virtuelles Volumen und -Masse

uk: вхід без авторизації


uk: вхід без авторизації

name: Optimaler Froude'scher Wirkungsgrad bei sehr kleinen Reynoldszahlen


3 and 4 level lasers

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Application to wave packets

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Applications: Coherent control

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


uk: Комп'ютерні науки

description: A recording of a presentation from Mark Finnane for the December 2018 AVSIG meeting

uk: вхід без авторизації

name: Terminology and classification in the Prosecution Project

Australian Research Data Commons (ARDC)

addressCountry: AU

addressLocality: Melbourne

addressRegion: Victoria


Applications: Laser cooling

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Applications: Pulsed lasers

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Atomic mass units, isotopes

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Atoms in Electric and Magnetic Fields

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Atoms with Multiple Electrons

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Bohr model of the hydrogen atom

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Bohr's Model Of The Atom

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації


Brownian motion as evidence for atoms

uk: Фізика, Астрономія

uk: Математика, природничі науки

uk: вхід без авторизації