List of videos

"Streaming: Why should I care? [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] You think, all that hype about streaming solutions does not affect you? I thought so also. But when playing around with that topic for some time, I realized that it sheds a different light on many topics I struggled with for some time. In this talk I want to share with you what I discovered when switching from a from a database centric view to stream oriented processing. Splitting your application in smaller services gets easier as you have more natural boundaries You have more options to run different data schema versions in different services (instead of one central db upgrade) More scaling possibilities Operations improvements For sure, streaming does not solve any problem, but much more than I thought before. And in python you have good support with many streaming clients. I will give some examples and comparisons for working with Kafka and Avro Schemas. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"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. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"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 process License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"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! License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Type Annotations in Python 3: Whats, whys & wows! [EuroPython 2017 - Talk - 2017-07-13 - Arengo] [Rimini, Italy] Python 3.5 introduced support for ""type hints"" (or annotations), which allows us to annotate our code with useful bits of information without affecting its runtime behavior. Type hints don't enforce a particular use case and can therefore be used for many purposes. In my talk, I will explain some of the use cases of type hints, and show how we can use them to e.g. make our code more secure or teach it new tricks. We will have a look at popular libraries that help us to use the power of type hints (e.g. mypy), but we will also see how we can build our own extensions on top of the type hint system. After the talk you should walk away with a solid (basic) understanding of type hints in Python and an idea of how they might be useful to you now or in the future. Typing and type hints in programming languages Short history and motivation of type hints in Python Basics of type hint syntax and semantics Use cases for type hints Type hints for code analysis: mypy and similar libraries Building new stuff with type hints in Python Summary, Outlook & Further Reading License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"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 NetworkX License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Lightning Talks [EuroPython 2017 - - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"Using Python and microservices to fuel WebPush at Mozilla [EuroPython 2017 - Talk - 2017-07-13 - Anfiteatro 2] [Rimini, Italy] This talk will focus on how python and microservices fuel webpush in the storage team of Mozilla . It will also act as a good introduction to webpush technology and how to integrate it into web apps. The talk will cover: How webpush works? The roles played by the various players involved (service workers and push servers and how they interact to bring about the webpush magic). The idea behind the service, WebPush Channels which is at the heart of webpush at Mozilla's storage team. How the service was developed will be covered in detail. How developers can use WebPush Channels to integrate web push in their web apps. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/

"How to apply deep learning for 3D object [EuroPython 2017 - Talk - 2017-07-13 - Arengo] [Rimini, Italy] I talk about the ""How to achieve the 3D object recognition accuracy 80%(40 category) for 3month " Deep Learning is the good technique for image recognition and speech recognition. And it apply the other field. Many people try to apply the Deep Learning, but it is difficult to make a result. In my situation, I have enough knowledge about the 3D object and label data. I'll talk about the how to achieve the 80 % (40 category) In My approach 1: Getting the Information 1.1: How to choose the information 1.2: How to choose framework 2: Getting the Data 2.1: Public data 2.2: How to make the own data 3: Try small 3.1: Trying the small data set 3.2: Trying the train and predict 4: Deciding the direction focus 4.1: Choose what you can control 5: Prioritizing with high certainty 5.1: Pre-process 5.2: Improve the train speed 6: Increasing the challenge times 6.1: Using the GPU 6.2: CPU optimization 6.3: multi process 6.4: resource 7: Parameter Tuning 7.1: Improve Model Versatility or Improve Data Versatility 7.2: Model Tuning 7.2.1: RandomDropOut 7.2.2: LeakyRelu 7.3: Data Argumantion 8: Product 8.1: Minimum function 8.2: Using Docker I hope to people who want to apply Deep Learning for the 3D model License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2017.europython.eu/en/speaker-release-agreement/