Karpathy's Free 'Neural Networks: Zero to Hero' Course Takes Learners From Backpropagation to Building GPT From Scratch

Andrej Karpathy's free 'Neural Networks: Zero to Hero' course on GitHub is empowering learners worldwide to build GPT models from scratch through 8 hands-on video lectures covering everything from backpropagation to Transformer architecture and Byte Pair Encoding tokenization.

• Karpathy's 'Neural Networks: Zero to Hero' is a free, open-source course on GitHub featuring 8 video lectures that guide learners from basic backpropagation all the way to building a GPT model and tokenizer from scratch.
• The course progresses through key deep learning concepts including MLP construction, activation functions, batch normalization, backpropagation, WaveNet-style CNNs, and Transformer architecture, with hands-on Jupyter notebooks accompanying each lecture.
• The latest lecture covers GPT tokenization, explaining Byte Pair Encoding and how tokenization issues contribute to many known LLM quirks, with supporting code and a Google Colab notebook available for hands-on practice.