ml fundamentals

machine-learning-fundamentals.md

ML-Fundamentals

• ConvNetJS, DeepLearning in Your Browser
• TensorFlow Examples
• Code for Hands-on ML using Scikit-Learn, Tensorflow, and Keras
• TensorFlow Tutorials
• Code for ML with PyTorch and Sci-kit Learn
• Large Model Parallelism Notebook
• Datascience IPython Notebooks
• Code for Artificial Intelligence, A Modern Approach
• Christopher Olah's Blog
• Code for NNs and DeepLearning book
• ML Fundamentals lectures
• The Unreasonable Effectiveness of RNNs, Andrej Karpathy
• Minimal and Clean RL examples
• Python NumPy Tutorial, Stanford
• Which GPUs to get for Deep Learning
• The Bitter Lesson
• Deep Learning Tuning Playbook, Google Research
• Micrograd, Andrej karpathy fav <3
• Automatic Differentiation, Mark Saroufim
• Machine Learning Tutorials
• What is Softmax? Sebastian Raschka
• Yes you should understand backprop, Andrej Karpathy
• Train with mixed-precision, Nvidia
• The Vanishing Gradient Problem
• Image Kernels
• Distill
• Making Deep Learning go Brrr from First Principles
• Circuits in NNs, OpenAI
• Too much efficiency makes everything worse: overfitting and the strong version of Goodhart's law
• Diffusion Models from Scratch
• Perspectives on Diffusion
• The Matrix Calculus You Need For Deep Learning, Jeremy Howard
• Attention is Off By One, Evan Miller
• RWKV Explained
• The Hardware Lottery, Sarah Hooker
• Recurrent Neural Networks, Stanford CS-230
• Hyena Hierarchy: Towards Larger Convolutional Language Models
• Simplifying S4
• No SRAM Scaling Implies on More Expensive CPUs and GPUs - TSMC
• Optimizing LLM Latency, Hamel Husain
• SLURM Survival Guide
• LLM Training, Replit
• RLHF, Chip Huyen

For more, look into starred repos on my profile ;)