NumPy looks like a calculator and behaves like a memory model. 🧮

Most tutorials start with functions. This one starts with the five decisions NumPy made before you opened it: an array is one block of memory, shape is a contract between arrays, a slice is a view you can write through, the loop belongs in C, and the axis tells you which direction collapses.

15 chapters, 25 runnable examples, and every output copied from a real execution on NumPy 2.0.2. 🚀

Arrays, shapes and the machinery under the math:
https://enghusseinsheikho.substack.com/p/numpy-the-mental-model?r=3hwnr8&utmcampaign=post&utmmedium=web&showWelcomeOnShare=true

https://t.me/CodeProgrammer ⭐️

🔖 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

https://t.me/CodeProgrammer 😍

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 🤩

If you trade cryptocurrencies and are feeling confused about the current market, I strongly recommend you join the group I’ve mentioned

The group shares the following content every day:
✅ One trading signal a day (with a success rate of up to 85%)
✅ Real-time updates on cryptocurrency market trends
✅ Personalised, one-to-one technical trading advice

Whether you’re a beginner or an experienced trader, you’re welcome to join the group to exchange ideas and gain useful insights

Join now；https://t.me/+mEow9q0UJNhiYjg0