Essential books, papers, and materials for learning LLMs
These carefully selected books provide comprehensive coverage of natural language processing, deep learning fundamentals, and the latest developments in transformer architectures and large language models.
This book provides an authoritative exploration of the design, training, evolution, and application of LLMs. It begins with an overview of pre-trained language models and Transformer architectures, laying the groundwork for understanding prompt-based learning techniques. Next, it dives into methods for fine-tuning LLMs, integrating reinforcement learning for value alignment, and the convergence of LLMs with computer vision, robotics, and speech processing. The book strongly emphasizes practical applications, detailing real-world use cases such as conversational chatbots, retrieval-augmented generation (RAG), and code generation. These examples are carefully chosen to illustrate the diverse and impactful ways LLMs are being applied in various industries and scenarios.
This book also helps you: Understand the architecture of Transformer language models that excel at text generation and representation, Build advanced LLM pipelines to cluster text documents and explore the topics they cover, Build semantic search engines that go beyond keyword search, using methods like dense retrieval and rerankers, Explore how generative models can be used, from prompt engineering all the way to retrieval-augmented generation and Gain a deeper understanding of how to train LLMs and optimize them for specific applications using generative model fine-tuning, contrastive fine-tuning, and in-context learning.
A detailed survey on large language models understanding
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