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Data Science, End to End — Essentials, Vol III: Deep Learning, NLP & LLMs
Neural Networks, NLP, LLMs & Shipping to Production
This book is 100% completeLast updated on 2026-10-16
From a single neuron to transformer-based LLMs in production — the deep-learning, NLP, and shipping capstone of the Data Science, End to End series, derived from first principles in Python.
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About the Book
I taught data science for years and kept wishing for one honest, end to end reference, so I wrote it. This is Volume III of three, and it goes from a single neuron to transformer based language models in production: deep learning from first principles (backpropagation, gradient descent and modern optimizers, CNNs for vision, RNNs and sequence models), then attention and the Transformer, then modern NLP and LLMs (tokenization and embeddings, pretraining and fine-tuning, prompting, retrieval augmented generation, evaluation), and finally how to actually ship, serve and monitor them. Every topic opens with a picture you can feel, then names the technical idea behind it. Each volume stands alone; together they run from raw data to large language models in production.
Author
About the Author
Archit Sharma
Archit Sharma is a data science educator and the author of the "Data Science, End to End — Essentials" series. He teaches data science from first principles — the mathematics shown honestly, every concept implemented in clean, idiomatic Python — and writes for practitioners who want to understand what they build, not just copy it. His work spans the full pipeline: the Python, statistics, and SQL foundations; the machine-learning core from regression to gradient boosting; and modern deep learning, NLP, and large language models, through to shipping them in production.
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