Machine Learning with Python

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💾 TensorTonic — a platform with 200+ algorithms and tasks for ML developers What's inside: ▶️ Analysis of research and step-by-step reproduction of model architectures; ▶️ Explanation of topics and concepts with interactive visualizations; ▶️ A progress and achievement system — what would we do without gamification. A great option to hone your ML skills in the evening ⚡ https://www.tensortonic.com/

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Classical filters & convolution: The heart of computer vision Before Deep Learning exploded onto the scene, traditional computer vision centered on filters. Filters were small, hand-engineered matrices that you convolved with an image to detect specific features like edges, corners, or textures. In this article, we will dive into the details of classical filters and convolution operation - how they work, why they matter, and how to implement them. More: https://www.vizuaranewsletter.com/p/classical-filters-and-convolution

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📱 Python enthusiasts, this is for you — 15 BEST REPOSITORIES on GitHub for learning Python ▶️ Awesome Python — https://github.com/vinta/awesome-python — the largest and most authoritative collection of frameworks, libraries, and resources for Python — a must-save ▶️ TheAlgorithms/Python — https://github.com/TheAlgorithms/Python — a huge collection of algorithms and data structures written in Python ▶️ Project-Based-Learning — https://github.com/practical-tutorials/project-based-learning — learning Python (and not only) through real projects ▶️ Real Python Guide — https://github.com/realpython/python-guide — a high-quality guide to the Python ecosystem, tools, and best practices ▶️ Materials from Real Python — https://github.com/realpython/materials — a collection of code and projects for Real Python articles and courses ▶️ Learn Python — https://github.com/trekhleb/learn-python — a reference with explanations, examples, and exercises ▶️ Learn Python 3 — https://github.com/jerry-git/learn-python3 — a convenient guide to modern Python 3 with tasks ▶️ Python Reference — https://github.com/rasbt/python_reference — cheat sheets, scripts, and useful tips from one of the most respected Python authors ▶️ 30-Days-Of-Python — https://github.com/Asabeneh/30-Days-Of-Python — a 30-day challenge: from syntax to more complex topics ▶️ Python Programming Exercises — https://github.com/zhiwehu/Python-programming-exercises — 100+ Python tasks with answers ▶️ Coding Problems — https://github.com/MTrajK/coding-problems — tasks on algorithms and data structures, including for preparation for interviews ▶️ Projects — https://github.com/karan/Projects — a list of ideas for pet projects (not just Python). Great for practice ▶️ 100-Days-Of-ML-Code — https://github.com/Avik-Jain/100-Days-Of-ML-Code — machine learning in Python in the format of a challenge ▶️ 30-Seconds-of-Python — https://github.com/30-seconds/30-seconds-of-python — useful snippets and tricks for everyday tasks ▶️ Geekcomputers/Python — https://github.com/geekcomputers/Python — various scripts: from working with the network to automation tasks React ♥️ for more posts like this 💛

🗂 Harvard has released a textbook on ML systems — from autograd to production Not just another "what is a neural network" course — this is about how to build combat-ready ML systems around models. What's inside: ▶️ Building autograd, optimizers, attention, and mini-PyTorch from scratch; ▶️ Batches, computational accuracy, architectures, and training; ▶️ Performance optimization, hardware acceleration, and benchmarking. You can read the book and the code for free right now. ✈️ Link to GitHub https://github.com/harvard-edge/cs249r_book