Free Download Quick Start Guide to Large Language Models: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI (Addison-Wesley Data & Analytics), 2nd Edition by Sinan Ozdemir
English | October 13th, 2024 | ISBN: 0135346568 | 384 pages | True EPUB (Retail Copy) | 42.16 MB
The Practical, Step-by-Step Guide to Using LLMs at Scale in Projects and Products
Large Language Models (LLMs) like Llama 3, Claude 3, and the GPT family are demonstrating breathtaking capabilities, but their size and complexity have deterred many practitioners from applying them. In Quick Start Guide to Large Language Models, Second Edition, pioneering data scientist and AI entrepreneur Sinan Ozdemir clears away those obstacles and provides a guide to working with, integrating, and deploying LLMs to solve practical problems.
Ozdemir brings together all you need to get started, even if you have no direct experience with LLMs: step-by-step instructions, best practices, real-world case studies, and hands-on exercises. Along the way, he shares insights into LLMs' inner workings to help you optimize model choice, data formats, prompting, fine-tuning, performance, and much more. The resources on the companion website include sample datasets and up-to-date code for working with open- and closed-source LLMs such as those from OpenAI (GPT-4 and GPT-3.5), Google (BERT, T5, and Gemini), X (Grok), Anthropic (the Claude family), Cohere (the Command family), and Meta (BART and the LLaMA family).
* Learn key concepts: pre-training, transfer learning, fine-tuning, attention, embeddings, tokenization, and more
* Use APIs and Python to fine-tune and customize LLMs for your requirements
* Build a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI Agents
* Master advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot prompting
* Customize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAI
* Construct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasets
* Align LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5
* Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mind
* Diagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks
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