Description: Further DetailsTitle: Quick Start Guide to Large Language ModelsCondition: NewSubtitle: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AIISBN-10: 0135346568EAN: 9780135346563ISBN: 9780135346563Publisher: Addison WesleyFormat: PaperbackRelease Date: 11/06/2024Description: 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 moreUse APIs and Python to fine-tune and customize LLMs for your requirementsBuild a complete neural/semantic information retrieval system and attach to conversational LLMs for building retrieval-augmented generation (RAG) chatbots and AI AgentsMaster advanced prompt engineering techniques like output structuring, chain-of-thought prompting, and semantic few-shot promptingCustomize LLM embeddings to build a complete recommendation engine from scratch with user data that outperforms out-of-the-box embeddings from OpenAIConstruct and fine-tune multimodal Transformer architectures from scratch using open-source LLMs and large visual datasetsAlign LLMs using Reinforcement Learning from Human and AI Feedback (RLHF/RLAIF) to build conversational agents from open models like Llama 3 and FLAN-T5Deploy prompts and custom fine-tuned LLMs to the cloud with scalability and evaluation pipelines in mindDiagnose and optimize LLMs for speed, memory, and performance with quantization, probing, benchmarking, and evaluation frameworks "A refreshing and inspiring resource. Jam-packed with practical guidance and clear explanations that leave you smarter about this incredible new field."--Pete Huang, author of The Neuron Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.Language: EnglishCountry/Region of Manufacture: USAuthor: Sinan OzdemirGenre: Computing & InternetBook Series: Addison-Wesley Data & Analytics SeriesItem Height: 231mmItem Length: 181mmItem Width: 18mmItem Weight: 623gRelease Year: 2024 Missing Information?Please contact us if any details are missing and where possible we will add the information to our listing.
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Book Title: Quick Start Guide to Large Language Models
Title: Quick Start Guide to Large Language Models
Subtitle: Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tunin
ISBN-10: 0135346568
EAN: 9780135346563
ISBN: 9780135346563
Release Date: 11/06/2024
Release Year: 2024
Country/Region of Manufacture: US
Genre: Computing & Internet
Number of Pages: 384 Pages
Language: English
Publication Name: Quick Start Guide to Large Language Models : Strategies and Best Practices for ChatGPT, Embeddings, Fine-Tuning, and Multimodal AI
Publisher: Addison Wesley Professional
Publication Year: 2024
Subject: Intelligence (Ai) & Semantics, Natural Language Processing, General, Programming Languages / Python
Item Height: 0.8 in
Type: Textbook
Item Weight: 20.8 Oz
Subject Area: Mathematics, Computers
Author: Sinan Ozdemir
Item Length: 9.1 in
Series: Addison-Wesley Data and Analytics Ser.
Item Width: 7 in
Format: Trade Paperback
