List of videos

Tech for Good: Build the world you want to live in [EuroPython 2021 - Talk - 2021-07-29 - Ni] [Online] By Alexys Jacob, Sébastien Crocquevieille, Romain Meson, Margaux Levisalles, Brieuc Le Bars Through its 1000mercis Impacts initiative, Numberly has been actively working with tech and non tech associations over the years for social, environmental and educational causes. As part of these commitments, we want to hand over the stage of this talk to some of our close partners. - Margaux from Latitudes.cc will introduce you to the various ways technology can be used for general interest. - Brieuc from Code Phoenix will share his great experience in coding teaching in prisons to help rehabilitation. - Souad from Descodeuses will explain how her association drives more women from disadvantaged neighborhoods in tech jobs. This talk is for all of you who are curious to know how to use your skills for a greater good. Join us to hear and share about Tech for Good! License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Build Your First Cyber Forensic Application using Python [EuroPython 2021 - Talk - 2021-07-29 - Ni] [Online] By Gajendra Deshpande A recent study by CheckPoint Research has recorded over 1,50,000 cyber-attacks every week during the COVID-19 pandemic. There has been an increase of 30% in cyber-attacks compared to previous weeks. The pandemic has been the main reason for job loss and pay cuts of people and has led to an increase in cybercrimes. Examples of cyber-attacks include phishing, ransomware, fake news, fake medicine, extortion, and insider frauds. Cyber forensics is a field that deals with the investigation of digital crimes by analyzing, examining, identifying, and recovering digital evidence from electronic devices and producing them in the court of law. Python has a great collection of built-in modules for digital forensics tasks. The talk begins with an introduction to digital crimes, digital forensics, the process of investigation, and the collection of evidence. Next, I will cover the various python modules and built-in functions required to build your first cyber forensic application. The modules covered in the discussion are pyscreenshot, PIL, secrets, argparse, hashlib, os,csv, logging, time, sys, stat and NLTK. Finally, I will demonstrate using code walk through the sample cyber forensic application. Outline 1. Introduction to digital crimes, digital forensics, the process of investigation, and the collection of evidence. (03 Minutes) 2. Setting up Python for forensics application development (02 Minutes) 3. Built-in functions and modules for forensic tasks (05 Minutes) 4. Forensic Indexing and searching (03 Minutes) 5. Forensic Evidence extraction (03 Minutes) 6. Using Natural Language Tools in Forensics (05 Minutes) 7. Code walkthrough of sample forensic application (08 Minutes) 8. Conclusion and Next steps (01 Minutes) 9. Questions and Answers (05 Minutes) License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

EuroPython 2022: Help us build the next edition! [EuroPython 2021 - EuroPython session - 2021-07-29 - Ni] [Online] By Marc-Andre Lemburg License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Adventures in Real-time Python NoSQL-style [EuroPython 2021 - Talk - 2021-07-29 - Ni] [Online] By Christoph Dr. Zimmermann The presentation introduces RedisGears, a novel approach to Python programming on the server side of an open source real-time NoSQL database. Ten years in the making, redis has achieved a significant following among the community concerned with ultra-fast processing of NoSQL-based data. With the advent of modules a few years back, redis can be further extended to bridge the gap between the comprehensive set of data structures already offered by the server core and application-specific requirements such as graphs, timeseries data or full-text search capabilities. RedisGears is such a module. At its core, it offers a flexible infrastructure allowing the combination of different modules in addition to scheduling, mapping, filtering and other functionality to process data on the server side. In addition, it allows the execution of Python code on server side, yielding an ultra-performant and flexible approach to the implementation of business logic as part of an application code base. The presentation is structured as follows: after a short introduction to redis, the main architecture of RedisGears is discussed in addition to an overview of how to use Python with RedisGears. Benchmark figures detail the performance benefits from such an approach. The focus of this presentation is not only the theory behind approach but rather hands-on, using code examples wherever possible. A basic understanding of Python is required for this presentation, basic redis exposure and general NoSQL know-how optional but beneficial. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Sweeter debugging and benchmarking with ycecream [EuroPython 2021 - Talk - 2021-07-29 - Ni] [Online] By Ruud van der Ham Even if you use a debugger, it is often required to print some variables or expressions, preferably with their name and sometimes even the location. The normal way to do that is with a print statement, like | print("hello= ", hello) or with Python >= 3.8: | print(f"hello=") Ycecream makes this easier with a simple call to y: | y(hello) and then you will get | y| hello: "world" But you can also add the location in the program where this debug call is coming and get something like | y| #6 in testproc() hello: "world" But ycecream can do much more to help to debug and benchmark Python programs. In this presentation, I will live code to demonstrate ycecream, There are some similar packages out there, like IceCream and show. In this talk I will point out some of the main differences. More information can be found here: www.github.com/salabim/ycecream License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Learn from LL(1) to PEG parser the hard way [EuroPython 2021 - Talk - 2021-07-29 - Ni] [Online] By Kir Chou Detailed description In late 2019, Guido van Rossum wrote a series of articles about PEG parser and gave the talk "Writing a PEG parser for fun and profit" in a couple of conferences, such as North Bay Python, mentioned the motivation of his research and prototype of PEG parser. In late 2020, other authors of PEP 617, Pablo Galindo and Lysandros Nikolaou, (created around mid of 2020) were interviewed on Podcast.init to talk about the result of the new PEG parser in CPython. Above contents assume the audience with solid compiler fundamental knowledge, but the fact is most People, including this talk's speaker, are not specialized in compiler. And another fact is most of the compiler class in school around the world only covers traditional top-down and bottom-up parsing techniques, not to mention PEG parser that appeared in the early 2000s. For the above reasons, the speaker will stand on the shoulders of giants and share his hard way after a few months of study. He will talk about the fundamentals of Parser (part of compiler frontend), including CFG, traditional parsing techniques, PEG, Packrat parser, and PEG parser in CPython. Objective If you have zero compiler background, you can at least learn the frontend compiler knowledge and how it is used in Python. If you have learned fundamental compiler knowledge in university, this talk will make you recall the traditional top-down and bottom-up parser in the class, and additionally add some relatively new ideas that appear in the early 2000s. If you are a compiler master and know the Packrat parsing research done by Bryan Ford, this talk may still provide you a brief idea about the reason why Guido van Rossum picked LL(1) initially, and PEG parser later. Outline - Opening + Self-intro - Motivation - What is parser in CPython? - Parser 101 - CFG - Parser 101 - Traditional top-down/bottom-up parser - Parser 102 - PEG and PEG parser - Parser 102 - Packrat parser - CPython’s PEG parser - Take Away License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Data lake: Design for schema evolution [EuroPython 2021 - Talk - 2021-07-29 - Parrot [Data Science]] [Online] By Prakshi Yadav Designing a data lake necessitates well-researched storage, management, scalability, and availability solutions. However, managing schema evolution remains a difficult task. The structure of data differs from one company to the next, making it difficult to generalize a solution to the schema evolution problem. At Episource, we faced a similar challenge - our data of interest is the output from our NLP engine. Episource's machine learning and natural language processing platform processes millions of pages of medical documents, with up to 15 ML/DL models working together to produce the results. The result of such a challenging pipeline is a complex nested JSON series. With each major update, our NLP engine evolves, causing the inference data structure to evolve as well. As data grew in size and complexity, storing it and making it searchable became a pressing necessity. We needed a solution that kept schema compatibility, versioning, and data integrity intact. We wanted to make sure data reads and writes were unaffected by the Schema mismatch problem. After several iterations and proofs of concept, we settled on a solution that uses the AVRO format to evolve our data's schema. Avro is a format similar to Parquet but can also accommodate schema evolution. To keep track of changes made to the system, schema versions are saved in a Schema registry. To read the AVRO data stored in S3, our data lake uses Athena, a distributed SQL engine based on Presto. The solution makes use of python libraries to glue various components of this pipeline. The following are some of the things that a participant can expect to learn during this talk: In a data lake, best practices for storage, control, scalability, and availability Managing schema evolution in a data lake The ability to use both "schema-on-write" and "schema-on-read" License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

Sound Event Detection with Machine Learning [EuroPython 2021 - Talk - 2021-07-29 - Parrot [Data Science]] [Online] By Jon Nordby Sound Events (or Audio Events or Acoustic Events) are individual distinct sounds. This could be the pop of roasting popcorn kernels, the cough of a patient, a car that is passing by on a road, or the sound of an alarm in an office building. Sound Event Detection (SED) is the task of detecting such sounds, returning precise times that each kind (class) of sound occurs. It finds uses in music analysis, manufacturing, medicine and noise monitoring. We will show how to realize a basic Sound Event Detection system in Python, using fermentation tracking of beer brewing as a fun and practical example. The talk will cover all major parts of such a system, including: - Collecting and exploring a custom dataset - Setting up the supervised learning task from the dataset - Extracting spectrogram features from audio waveforms - Training a Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) - Running the trained model on an real-time audio stream - Processing model output probabilties into discrete events - Evaluate the performance of the resulting SED system Example code in Python covering these aspects will be provided. Libraries used with be Keras, TensorFlow and scikit-learn for machine learning, and pysoundfile, sounddevice and librosa for audio processing, with some numpy and pandas for general data manipulation. License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/

The Optimal Wedding (With Pyomo) [EuroPython 2021 - Talk - 2021-07-29 - Ni] [Online] By Tarik Berrada Your parents haven’t talked to each other for years. Your uncle Sam went to the same university as Mr. Finkelstein, while the Trautsohns share a common interest for photography with the Krazinskis. The N’Guyens had Covid, while the Lefebvres aren’t even vaccinated. You would like to achieve a reasonable mix of people knowing each other and of new acquaintances. How are you supposed to arrange the seating of the guests? Besides wedding planning, mathematical optimization has a wide range of applications including logistics, chemistry, energy system operation, genetics… In this talk, we’ll present how mathematical optimization problems can be formulated, fed with data and solved in Python using the open-source Pyomo package (http://www.pyomo.org/) and open-source optimization solvers such as the COIN-OR Branch-and-Cut Solver. This talk is aimed at a general audience familiar with Python and requires no mathematical optimization background, nor actual wedding experience. The repository to this talk: https://gitlab.com/Rikerl/optimalwedding License: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/ Please see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/