🔖 Interactive tool for learning machine learning

A Python library has been released on GitHub that allows you to create your own datasets and use them for machine learning experiments.

Features:
🔵 Create 2D datasets directly in Jupyter;
🔵 Draw points and assign classes using the mouse;
🔵 Export data to pandas and Polars;
🔵 Pass samples to scikit-learn;
🔵 Experiment with classification and regression;
🔵 Update model training when data changes;
🔵 Works in Jupyter, VS Code, and Google Colab.

There is a demo version that you can try without installation.

⛓Link to GitHub
https://github.com/koaning/drawdata

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Columbia University has published a detailed educational resource on the Transformer architecture 📚.

It covers the mathematical foundations of one of the key technologies in modern AI systems, from recurrent neural networks and the encoder-decoder architecture to attention mechanisms and self-attention.

Particular attention is paid to query, key, and value, scaled dot-product attention, multi-head attention, and softmax.

The main feature of the material is its mathematical approach. The authors use formulas, step-by-step derivations, diagrams, and examples to explain how the individual components of the Transformer are structured and how they interact with each other.

https://www.columbia.edu/~jsl2239/transformers.html

https://t.me/MachineLearning9

Deep Learning Cheat Sheet 🧑🏻‍💻

https://t.me/MachineLearning9 👾

Deep Learning Architecture

https://t.me/CodeProgrammer 🤩

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Linear Algebra Cheat Sheet

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