🔖 ML algorithms in visualizations

A useful repository that helps you understand how machine learning algorithms work – through interactive diagrams and step-by-step explanations.

You can run it in your browser or locally using Docker.

⛓ Link to GitHub
https://github.com/gavinkhung/machine-learning-visualized

A Collection of Machine Learning Libraries for Python 🤖

A large repository containing over 900 libraries and frameworks for machine learning. 📚

All projects are sorted by quality and popularity, which helps you quickly find the best tools for working with AI and ML. ⚙️

Repo: https://github.com/ml-tooling/best-of-ml-python?tab=readme-ov-file#vector-similarity-search-ann

#MachineLearning #Python #AI #DataScience #MLTools #Programming

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📚 "Natural Language Processing and Large Language Models" is a new open-access book from Springer, written by Chengqing Zong, Yang Zhao, and Yanjun Ma.

It's almost 400 pages long and provides an introduction to modern natural language processing and large language models.

Inside, you'll find information on: neural networks, distributed representations, language models, Transformers, BERT, GPT, tokenization, sentiment analysis, information extraction, text summarization, natural language understanding, machine translation, question answering, and RLHF.

In my opinion, this is a good reference guide for those who want to understand these topics without a very high barrier to entry. I would recommend it. ✨

https://link.springer.com/book/10.1007/978-981-92-0682-7

#NLP #LLM #ArtificialIntelligence #MachineLearning #DataScience #TechBooks

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Updated CS 8803 "Large Language Model" course at Georgia Tech for 2026.

The list of materials covers pre-training, Mixture of Experts (MoE), reasoning, reinforcement learning and self-play, agents, long context, scaling during inference, diffusion language models, safety, interpretability, and much more.

- https://cocoxu.github.io/CS8803-LLM-spring2026/

- https://docs.google.com/spreadsheets/d/1Oisf4imoNL3fs4UWGYAUlMCuYfCACMHCDb0iqEYU8wc/edit?usp=sharing

Don't forget to try it; it's free and includes most AI models.

City2Graph is a new Python library that transforms geospatial data, such as buildings, streets, and public transportation routes, into heterogeneous graphs.

It connects GeoPandas with PyTorch Geometric, allowing this type of urban data to be directly used in graph-based machine learning.

https://github.com/c2g-dev/city2graph