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aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Understanding Deep Learning - Simon J.D. Prince
Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
Translated【🔞🔞🔞 Contains images not suitable for minors】AI exploration and summary based on my strengths in programming, drawing, and writing: StableDiffusion is a powerful image generation model that can generate new images by evolving an existing image. ChatGPT is a Transformer-based language generation model that can automatically generate suitable articles for an input topic. And Github Copilot is an intelligent programming assistant that can speed up everyday programming activities.
🔥 LeetCode for PyTorch — practice implementing softmax, attention, GPT-2 and more from scratch with instant auto-grading. Jupyter-based, self-hosted or try online.
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
All notes and materials for the CS229: Machine Learning course by Stanford University
Learn to build your Second Brain AI assistant with LLMs, agents, RAG, fine-tuning, LLMOps and AI systems techniques.
Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)
Collection of tools and code examples to demonstrate best practices in using Amazon EC2 Spot Instances.