Because model.fit() isn’t an explanation. Contribute to Mathews-Tom/no-magic development by creating an account on GitHub.

The thing that would actually help an underperformer improve is to teach or (even better) show them how to do a better job and the same is true for models.

A four-step engineering process for software teams that don’t write code

This repository contains various advanced techniques for Retrieval-Augmented Generation (RAG) systems. The rag-cookbooks GitHub repository provides a collection of Jupyter notebooks offering tutorials, best practices, and practical use cases for implementing Retrieval Augmented Generation (RAG) systems.

A technique for deterministic labeling from stochastic models, with benchmarked Golang implementation.

The support agent Mimi. Contribute to Xe/mimi2 development by creating an account on GitHub.

Learn how to onboard AI coding agents with all the code, tech, and business context needed to rapidly develop your software project.

Multiple parallel Claude-Code sessions power a self-improving AI factory where agents write, review, and refine code.

A tiny example Autograd engine

Cuda implementation of Micrograd

MiniTorch is a diy teaching library for machine learning engineers who wish to learn about the internal concepts underlying deep learning systems. It is a pure Python re-implementation of the Torch API designed to be simple, easy-to-read, tested, and incremental. The final library can run Torch code.

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. - mlabonne/llm-course

Merge two embedding sequences regardless of modality, e.g., image with text in Stable Diffusion U-Net with encoder-decoder attention.