26 au 28 février, 2025
Montréal, Canada
Conférence Python
La liste des présentations est sujette à changement sans préavis.
Python
Learn about advanced profanity detection patterns beyond simple keyword search. My session will cover data collection and training of a profanity detection model, and it's integration with live Speech-to-Text AI technologies. We will also look at the challenges around handling live data.
Python
Python "batteries included" comes with powerful loop constructs, but knowing how to use them correctly can be tricky. This talk introduces the itertools module, which offers functions designed to make your loops smarter rather than harder.
We'll explore a variety of itertools functions with real-world examples.
By the end of this presentation, you will have a set of itertools functions to create more efficient and powerful loops in Python.
We'll explore a variety of itertools functions with real-world examples.
By the end of this presentation, you will have a set of itertools functions to create more efficient and powerful loops in Python.
Python
Comment utiliser l'IA générative sans tomber dans ses pièges ? Ce talk explore son rôle dans la production cinématographique, télévisuelle et marketing, en mettant en lumière réussites et échecs. Nous verrons comment elle peut aider à analyser des documents, valider des informations et automatiser des processus, tout en gardant le contrôle. Au-delà des solutions off the shelf, des exemples concrets montreront comment en tirer le meilleur parti.
Python
??Debuggers are indispensable tools for all Python developers, empowering them to conquer bugs and unravel complex systems. But have you ever wondered how they work? Curious about the implementation of features like conditional breakpoints and single stepping?
Join me for a talk in which we create our own debugger with conditional breakpoints, single stepping, and a Python-based debugging shell and learn a lot about debuggers along the way.
Join me for a talk in which we create our own debugger with conditional breakpoints, single stepping, and a Python-based debugging shell and learn a lot about debuggers along the way.
Python
20 years ago, the Python community was rightly proud of clear, transparent, obvious code. With growth of the language and the community, we have forgotten the importance of this. I'll discuss some of the technologies that make it too easy to write unclear, obfuscated Python code shrouded in implicit dependencies: decorators, active records, metaclasses, and more. And I will discuss ways back to the true path: how can we make our code clear again?
Python
Python has come a long way thanks to MyPy. In this topic you will learn how to adapt any Python code to My - so it becomes reliable and helps you identify intrinsic issues - many of which you had no idea you were ignoring all along. This talk is based on the experience of converting Snyk's Deepcode Python code base from zero to 100% MyPy safety.
Python
L’apprentissage par renforcement (Reinforcement Learning) est une approche de l'intelligence artificielle dans laquelle un agent apprend à prendre des décisions dans un environnement afin de maximiser une récompense cumulée. Le sujet aborderait les principes de base de cette approche, en s'appuyant sur des exemples pratiques avec Python et la librairie OpenAI Gym. Cette dernière offre une plateforme pour développer et tester des algo de RL
Python
RAG integrates external knowledge sources to improve performance by retrieving relevant documents during inference, making it ideal for tasks that require dynamic, up-to-date information. In contrast, Fine-tuning adapts pre-trained models to specific tasks through supervised learning, optimizing performance within static, well-defined domains. We analyze the trade-offs in terms of data requirements, deployment flexibility, cost, and use cases.
Python
Have you ever encountered code having so many conditions and processing scenarios that it was impossible to maintain and extend t?
What about replacing it with a self-improving automatically generated algorithm?
With the rise of ML algorithms, artificial neural networks and LLMs, programming complex business rules and classification services can be successfully replaced with ML-based solutions. Let's see how to do it in your project!
What about replacing it with a self-improving automatically generated algorithm?
With the rise of ML algorithms, artificial neural networks and LLMs, programming complex business rules and classification services can be successfully replaced with ML-based solutions. Let's see how to do it in your project!
Python
Mock objects are a great trick for testing code that cannot be tested any other way. Unfortunately, a lot of developers have interpreted that to mean that mock objects should be used for testing any and all code. This leads to tests that are not very thorough, tightly coupled to the code under test, hard to understand, and hard to modify. I'll discuss some techniques that help you write better tests -- most importantly, fakes and stubs.
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