A personal study repository tracking my journey into large language models, from first principles through to building and fine-tuning my own.
Code, notes, and experiments produced while working through:
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Deep Learning with Python, 3rd Edition — François Chollet (Manning) Foundations: neural networks, backpropagation, training loops, and the Keras/TensorFlow ecosystem that underpins modern deep learning.
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Hands-On Large Language Models — Jay Alammar & Maarten Grootendorst (O'Reilly) Applied LLMs: tokenisation, embeddings, transformers, semantic search, retrieval-augmented generation (RAG), agents, and fine-tuning.
Everything here is written by me as I learn — expect rough edges, exploratory notebooks, and notes-to-self alongside cleaner reference implementations.