Скачать или смотреть The Hundred-Page Language Models Book, Burkov

This document provides excerpts from "The Hundred-Page Language Models Book" by Andriy Burkov, a concise guide to machine learning and natural language processing. The book is lauded for its conciseness and clarity, making complex AI concepts accessible to a broad audience, including software developers, data scientists, and machine learning engineers.

The podcast covers foundational machine learning concepts such as linear models, neural networks, and gradient descent, before progressing to language modeling techniques like Bag of Words, n-grams, and word embeddings. It explains the architecture and training of recurrent neural networks (RNNs) and Transformer models, including Rotary Position Embedding and self-attention. The excerpts also address practical aspects of large language models (LLMs), such as supervised finetuning, sampling methods, and evaluation metrics like ROUGE and Elo ratings, concluding with a discussion on ethical considerations and future advancements in the field and fundamental concepts from machine learning basics to advanced Transformer architectures and practical applications.

Full Glossary of Terms List, Plus a Quiz Guide (Free)
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