| Last month I hosted an in-person workshop about building your own large language model without any math or ML prerequisites. It covers everything from machine learning fundamentals, deep neural networks, transformer architecture, and pre/post-training. I’m releasing recordings and training materials for you to watch! >> https://go.justinangel.ai/video-1 << The workshop’s goal is to grok all parts of modern LLM development. Each section of the workshop has slides teaching the concepts, followed by excel-by-hand exercises developing intuition for the math, and then coding tutorials. One participant, Emily HK, noted: “The best part of this workshop is that all the content is still available online for me to refresh my memory at any point”. Well, now you have access to these materials as well! * 23 Workshop Videos @ https://go.JustinAngel.ai/playlist * 250-page Slide Deck @ https://go.JustinAngel.ai/deck * 50 Excel and Code Exercises @ https://go.JustinAngel.ai/drive YOUTUBE LINKS 1. Sampling Large Language Models https://go.justinangel.ai/video-1 2. Reverse Engineering Large Language Model https://go.justinangel.ai/video-2 3. Perceptrons: wx+b https://go.justinangel.ai/video-3 4. Activation Functions: ReLU, GELU, SwiGLU https://go.justinangel.ai/video-4 5. GPU Coding: PyTorch, torch.compile(), fused kernels, CUDA, Triton https://go.justinangel.ai/video-5 6. MLPs/FFNs: Multi-input, Multi-Layer Perceptrons, Feed-Forward Networks https://go.justinangel.ai/video-6 7. Loss Functions: Residual errors, RMSE, Cross Entropy, Loss Landscapes https://go.justinangel.ai/video-7 8. Backpropagation: Training loops, Optimizers, Learning Rate, Batch Size https://go.justinangel.ai/video-8 9. Saving & Loading Models https://go.justinangel.ai/video-9 10. Initialization: Kaiming, Glorot https://go.justinangel.ai/video-10 11. Residuals: Addition, Scaling, Gated, Concatenation https://go.justinangel.ai/video-11 12. Normalization: Pre-norm vs. Post-norm, RMSNorm, BatchNorm, LayerNorm https://go.justinangel.ai/video-12 13. Regularization: Dropout, Gradient Clipping, Weight Decay https://go.justinangel.ai/video-13 14. SoftMax https://go.justinangel.ai/video-14 15. Tokenizers: By Character, By Word, BPE, SentencePiece https://go.justinangel.ai/video-15 16. Embeddings: Absolute vs. Learned, Sinusoidal vs. RoPE https://go.justinangel.ai/video-16 17. Attention: MHA, GQA, MQA, MLA https://go.justinangel.ai/video-17 18. Transformers https://go.justinangel.ai/video-18 19. Pre-training: Data Sources, Datasets, HTML Cleaning, Quality Filtering, Sharding https://go.justinangel.ai/video-19 20. Evaluation: Leaderboards, Benchmarks, Verifiers vs LLM-as-Judge https://go.justinangel.ai/video-20 21. Instruction Tuning: Alpaca & Other Formats, Self Instruct, Capabilities https://go.justinangel.ai/video-21 22. Reinforcement Learning: Policy Optimization, SimPO https://go.justinangel.ai/video-22 23. What We Didn't Cover: Scaling https://go.justinangel.ai/video-23 |