List of videos

"Natural language processing with neural networks. [EuroPython 2019 - Talk - 2019-07-11 - Singapore [PyData track] [Basel, CH] By Hubert Bryłkowski Getting started with a natural language processing and neural networks is easier nowadays thanks to the numerous talks and tutorials. The goal is to dive deeper for those who already know the basics, or want to expand their knowledge in a machine learning field. The talk will start with the common use cases that can be generalized to the specific problems in a NLP world. Then I will present an overview of possible features that we can use as input to our network, and show that even simple feature engineering can change our results. Furthermore, I will compare different network architectures - starting with the fully connected networks, through convolution neural networks to recursive neural networks. I will not only considering the good parts, but also - what is usually overlooked - pitfalls of every solution. All of these will be done considering number of parameters, which transfers into training and prediction costs and time. I will also share a number of “tricks” that enables getting the best results even out of the simple architectures, as these are usually the fastest and quite often hard to beat, at the same time being the easiest to interpret. 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://ep2019.europython.eu/events/speaker-release-agreement/

"Building Industry 4.0 logistics applications with MicroPython and ESP32 MCUs [EuroPython 2019 - Talk - 2019-07-11 - Shanghai] [Basel, CH] By Florian Wahl In this session, we will report about a forklift hailing application we built using MicroPython and the ESP32-based prototyping hardware M5Stack. In a production facility, machine operators can hail a forklift using an ESP32 device running MicroPython. The device sends a request to a forklift dispatcher software, written in Python. The request is queued and subsequently, the next available forklift is notified and sent to the machine. Each forklift is also fitted with an ESP32 device. In this project, the prototyping hardware M5Stack was used. It features an ESP32 micro controller with Bluetooth, WIFI, GPIO, three buttons, and a display. Attendees will learn how MicroPython and prototyping hardware can be used to scale from an idea on the developers desk to a working system in a rough Industry 4.0 production environment. The talk will recap our journey with MicroPython, the lessons learned, and the pitfalls to avoid. Furthermore, the tooling setup will be introduced, including our M5Stack GUI-simulator to mock the M5Stack display. The central dispatch server is built using Python and deployed in a Docker container. Messages are exchanged using MQTT, a popular IoT message protocol. The talk will conclude with a live demonstration of the forklift hailing system. Attendees should be comfortable with the Python syntax but are not expected to have any MicroPython, MQTT, Docker or domain knowledge. Materials can be found at https://gitlab.com/fwahl/europython2019. 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://ep2019.europython.eu/events/speaker-release-agreement/

"“When a biologist met Python” [EuroPython 2019 - Talk - 2019-07-11 - Singapore [PyData track] [Basel, CH] By Maria Molina-Contreras Biology and computing are closer than we usually think, for example many algorithms are inspired in biology patterns, and complementary to that, researchers needs special algorithms to have a better understanding of our environment. Thus, there is a strong relation an dependency. In the past years, Biology has been transformed into computational biology. Therefore technological advances helps us to predict physical interactions between atoms and DNA, because we are being able to integrate information from biology into algorithms. Python has become a popular programming language in biosciences because it has a clean syntax that makes it easy to read language. In addition to this, there are many modules (toolkits) extending to different biological domains, like metabolomics, structure analysis, phylogenomics, molecular biology and others. Python is currently improving researcher’s workflow, helping us to focus on the theory or experimental part, instead of fighting with old buggy applications. This talk aims to be oriented to all audiences (with/without biological background) since we will go together through an amazing adventure into the natural sciences using tools like Biopython, Bokeh, Networkx, Ecopy and much more! Are you brave enough to follow me on this journey? 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://ep2019.europython.eu/events/speaker-release-agreement/ "

"How To Build a Python Microservice Without Losing a Job [EuroPython 2019 - Talk - 2019-07-11 - Shanghai] [Basel, CH] By Anton Caceres Sarcastic talk, sharing real-life experience on both technical and social aspects of doing an architecture migration to microservices without losing a job. Any change in software architecture is a significant time investment. Writing microservices in Python is a joy, but when you decide on it, there is often no way back. Therefore it is always an advantage to know what to expect in advance, not just from inspiring blog posts but also from the harsh reality. I would like to share typical pitfalls of choosing a framework stack, communication protocol, conventions, and deployment process — all covered by real projects. 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://ep2019.europython.eu/events/speaker-release-agreement/

"Building Data-Driven Client Relationship Management in Banking with Python [EuroPython 2019 - Talk - 2019-07-11 - Singapore [PyData track] [Basel, CH] By Paul Hughes This is a case study that documents how a small data science team in a big bank took on the challenge to transform a fragmented sales process into a data-driven one using Python and machine learning. This talk outlines the various ways Python has been instrumental in delivering a production solution that serves advisers and relationship manager on a continuous basis. The Challenge - A bank has many clients with diverse needs and cost pressures mean fewer advisers resulting in reduced client coverage. - Multiple sales channels and mixed service levels meant sales processes were uncoordinated and driven by heuristics and often very subjective. - And... Excel sheets everywhere! Solution - Go data-driven! - Learn from clients and understand product usage - Empower and inform advisers and call centre agents - Build a front-to-back sales process (no more Excels!) - How? With Python! The Python Bits - Scikit learn machine learning pipelines that implement two distinct approaches to product affinity in banking and wealth management - SQL Alchemy based API for data engineering and rapid prototyping of analytics - Pandas and Jupyter for development and collaboration - Luigi pipeline for daily processing of millions of transactions and engineering features - Extracting features from text with NLP (Spacy) - Delivering machine learning interpretability in production, e.g. with Random Forests and treeinterpreter - A Python module that we built with all the reusable bits: building training and prediction datasets, developing pipelines, generating monitoring data and enabling explainability 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://ep2019.europython.eu/events/speaker-release-agreement

"Practical decorators [EuroPython 2019 - Talk - 2019-07-11 - Shanghai] [Basel, CH] By Reuven Lerner Decorators are one of Python's most powerful features. But even if you understand what they do, it's not always obvious what you can do with them. Sure, from a practical perspective, they let you remove repeated code from your callables. And semantically, they let you think at a higher level of abstraction, applying the same treatment to functions and classes. But what can you actually do with them? For many Python developers I've encountered, decorators sometimes appear to be a solution looking for a problem. In this talk, I'll show you some practical uses for decorators, and how you can use them to make your code more readable and maintainable, while also providing more semantic power. Moreover, you'll see examples of things would be hard to do without decorators. I hope that after this talk, you'll have a good sense of how to use decorators in your own Python projects. 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://ep2019.europython.eu/events/speaker-release-agreement/

"Audio Classification with Machine Learning [EuroPython 2019 - Talk - 2019-07-11 - Singapore [PyData track] [Basel, CH] By Jon Nordby Sound is a rich source of information about the world around us. Modern deep learning approaches can give human-like performance on a range of sound classifiction tasks. This makes it possible to build systems that use sound to for example: understand speech, to analyze music, to assist in medical diagnostics, detect quality problems in manufacturing, and to study the behavior of animals. This talk will show you how to build practical machine learning models that can classify sound. We will convert sound into spectrograms, a visual representation of sound over time, and apply machine learning models similar to what is used to for image classification. The focus will be on Convolutional Neural Networks, which have been shown to work very well for this task. The Keras and Tensorflow deep learning frameworks will be used. Some tricks for getting usable results with small amounts of data will be covered, including transfer learning, audio embeddings and data augmentation. A basic understanding of machine learning is recommended. Familiarity with digital sound is a bonus. 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://ep2019.europython.eu/events/speaker-release-agreement/ "

"Maintaining a Python Project When It’s Not Your Job [EuroPython 2019 - Talk - 2019-07-11 - Shanghai] [Basel, CH] By Hynek Schlawack PyPI is a gold mine of great packages but those packages have to be written first. More often than not, projects that millions of people depend on are written and maintained by only one person. If you’re unlucky, that person is you! So how do you square delivering a high quality Python package you can be proud of and having only limited time at your disposal? The answer is not “try harder,” the answer is to do less. This talk will help you get there by talking about how you can make your life easier, remove causes of friction with your contributors, and empower said contributors to take over tasks that you can’t make time for anymore. 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://ep2019.europython.eu/events/speaker-release-agreement/

"Deploy Python to the cloud faster with Azure Serverless [EuroPython 2019 - Talk - 2019-07-11 - MongoDB] [Basel, CH] By Jeff Hollan Bringing your Python scripts or apps and running them in the cloud is easier than ever with serverless computing. In this talk, we’ll show how you can use Azure Functions to easily deploy and scale your Python workloads without having to manage any servers or pay for unused compute resources. We’ll also show how deep integration with Visual Studio Code offers a great local development experience with full support for debugging and testing your app, and allows you to seamlessly deploy your serverless code to the cloud. We’ll start by giving a brief overview of the value of serverless computing. Next, we'll create a brand new Python app in Visual Studio Code, and start to add in business logic and ML capabilities. Once we get the app running debugged locally, we’ll publish it to the cloud as a serverless function, and demonstrate the serverless scalability. Finally, we’ll walk you through the tools and capabilities you can leverage around monitoring and management of your Python Azure Functions. 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://ep2019.europython.eu/events/speaker-release-agreement/