💾 Free Deep Learning Course from NASA

We found a practical course on applying modern AI methods in astronomy and astrophysics.

It covers neural network architectures, generative models, training on scientific data, simulation-based inference, and research AI agents.

⛓ Link to the course
https://deeplearning4astro.com/

#AI #DeepLearning #NASA #Astronomy #MachineLearning #DataScience

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🔖 637 interactive modules on ML and AI

On VizLearn, you can visually explore:
🫡 Gradient descent;
🫡 SVM and PCA;
🫡 Bayes' theorem;
🫡 BPE tokenization;
🫡 Query, Key, and Value;
🫡 KV cache;
🫡 Quantization;
🫡 Neural networks and LLMs.

Change the input data and immediately see the results of the calculations.

⛓️ Link to the tutorial
https://enghusseinsheikho.substack.com/p/637-interactive-modules-on-ml-and?r=3hwnr8&utm_campaign=post&utm_medium=web

Agentic AI Roadmap 2026

https://t.me/DataAnalyticsX

The 25 Best Free Resources for Learning AI in 2026

Learning artificial intelligence in 2026 is both easier and harder than it was a couple of years ago. Easier, because the best courses from Stanford and MIT, books by leading researchers, and hands-on lessons from Hugging Face are all openly available. Harder, because there is now far too much material, and the models and tools change every week. We have collected 25 proven free resources and arranged them by stage: from mathematics to LLMs, agents, and reading the latest papers.

Read: https://enghusseinsheikho.substack.com/p/the-25-best-free-resources-for-learning?r=3hwnr8&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

🗂 500+ Cheat Sheets on Machine Learning

This repository contains materials on virtually everything: from basic algorithms and mathematics to neural networks, transformers, and advanced Deep Learning.

Each cheat sheet includes theory, formulas, Python examples, and tips on how to apply them.

⛓ You can find it here
https://github.com/gurovic/MLCheatSheets

#MachineLearning #DeepLearning #Python #DataScience #NeuralNetworks #AI

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10 Python libraries every AI engineer should know:


PyTorch: Deep learning models

Hugging Face Transformers: Pretrained AI models

LangChain: LLM apps & agents

LangGraph: Stateful agent workflows

OpenAI Python SDK: OpenAI model integrations

Pydantic: Data validation

Sentence Transformers: Text embeddings

FAISS: Vector similarity search

FastAPI: Web APIs

MLflow: Experiment tracking & ML lifecycle management

Anything else you'd add to this list?
https://t.me/CodeProgrammer

Oxford University has launched a free course on "Deep Learning Theory" for the 2026-2027 academic year.

The course focuses on the mathematical foundations of deep neural networks and includes 16 lectures that are freely accessible.

The program covers neural network architectures, nonlinear transformations, convolutional structures, pooling, optimization methods, dictionary learning, transferability of learned features, adversarial examples, random matrix theory, invariance learning, and other topics.

The lectures explore the mathematical theories that explain how deep neural networks learn and what accounts for their unique characteristics.

In my opinion, this is particularly valuable material for those who want to understand the mathematics that underlies modern AI systems.

https://courses.maths.ox.ac.uk/course/view.php?id=6621

https://t.me/CodeProgrammer

pandas — The Mental Model. A practical, example-driven guide in 18 chapters: how tables and indexes actually work, selection and filtering, groupby, reshaping, joins, time series, text, chaining, performance, and what changed in pandas 3.0. Every example is executed, with its real output shown, and the book closes with a one-page cheat sheet.

If the field is short, cut it to: A 31-page practical guide to pandas — 18 chapters, every example executed with real output, from indexes and selection to groupby, performance and pandas 3.0.

https://t.me/MachineLearning9