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65 books
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3 voters
Llm Books
Showing 1-50 of 263
AI Engineering: Building Applications with Foundation Models (Paperback)
by (shelved 12 times as llm)
avg rating 4.41 — 1,007 ratings — published
Build a Large Language Model (From Scratch)
by (shelved 12 times as llm)
avg rating 4.60 — 299 ratings — published
Hands-On Large Language Models: Language Understanding and Generation (Paperback)
by (shelved 11 times as llm)
avg rating 4.30 — 247 ratings — published
Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG (Paperback)
by (shelved 11 times as llm)
avg rating 4.12 — 52 ratings — published
Natural Language Processing with Transformers: Building Language Applications with Hugging Face (Paperback)
by (shelved 7 times as llm)
avg rating 4.39 — 211 ratings — published
The Hundred-Page Language Models Book: hands-on with PyTorch (The Hundred-Page Books)
by (shelved 4 times as llm)
avg rating 4.34 — 65 ratings — published
Learning LangChain: Building AI and LLM Applications with LangChain and LangGraph (Paperback)
by (shelved 4 times as llm)
avg rating 3.81 — 43 ratings — published
LLM Engineer's Handbook: Master the art of engineering large language models from concept to production (Kindle Edition)
by (shelved 4 times as llm)
avg rating 3.89 — 61 ratings — published
LLMs in Production: From language models to successful products (Paperback)
by (shelved 3 times as llm)
avg rating 4.11 — 27 ratings — published
Developing Apps with GPT-4 and ChatGPT: Build Intelligent Chatbots, Content Generators, and More (Paperback)
by (shelved 3 times as llm)
avg rating 3.73 — 100 ratings — published
Universal's Guide to LL.M. Entrance Examination, Including Previous Years Solved Papers (Paperback)
by (shelved 3 times as llm)
avg rating 3.54 — 13 ratings — published
Natural Language Processing in Action (Paperback)
by (shelved 2 times as llm)
avg rating 4.16 — 79 ratings — published
Qualcuno con cui correre (Paperback)
by (shelved 2 times as llm)
avg rating 4.09 — 11,495 ratings — published 2000
Some People Need Killing: A Memoir of Murder in My Country (Hardcover)
by (shelved 2 times as llm)
avg rating 4.21 — 10,241 ratings — published 2023
Tout le bleu du ciel (Kindle Edition)
by (shelved 2 times as llm)
avg rating 4.45 — 62,509 ratings — published 2019
Pedro Páramo (Paperback)
by (shelved 2 times as llm)
avg rating 4.05 — 103,158 ratings — published 1955
Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications (Kindle Edition)
by (shelved 2 times as llm)
avg rating 3.89 — 75 ratings — published
BUILDING A COMPREHENSIVE AI MODEL: Large Language Models, Generative AI, DeepSeek, Ethics & Accountability (The Architecture of Intelligence Book 1)
by (shelved 2 times as llm)
avg rating 5.00 — 3 ratings — published
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Paperback)
by (shelved 2 times as llm)
avg rating 4.45 — 1,079 ratings — published 2022
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs (Paperback)
by (shelved 2 times as llm)
avg rating 3.62 — 134 ratings — published
The Obscene Bird of Night (Paperback)
by (shelved 2 times as llm)
avg rating 4.21 — 4,780 ratings — published 1970
The God of Small Things (Paperback)
by (shelved 2 times as llm)
avg rating 3.96 — 330,121 ratings — published 1997
Designing Large Language Model Applications: A Holistic Approach to LLMs (Paperback)
by (shelved 2 times as llm)
avg rating 4.50 — 16 ratings — published
Building LLM Powered Applications: Create intelligent apps and agents with large language models (Paperback)
by (shelved 2 times as llm)
avg rating 3.58 — 33 ratings — published
Transformers for Natural Language Processing and Computer Vision: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 (Paperback)
by (shelved 2 times as llm)
avg rating 4.62 — 8 ratings — published
Practical Natural Language Processing: A Comprehensive Guide to Building Real-world NLP systems (Paperback)
by (shelved 2 times as llm)
avg rating 3.87 — 77 ratings — published
What Is ChatGPT Doing... and Why Does It Work? (Kindle Edition)
by (shelved 2 times as llm)
avg rating 3.85 — 1,597 ratings — published
Thinking, Fast and Slow (Hardcover)
by (shelved 2 times as llm)
avg rating 4.17 — 597,357 ratings — published 2011
A Conflict of Visions: Ideological Origins of Political Struggles (Paperback)
by (shelved 2 times as llm)
avg rating 4.31 — 4,822 ratings — published 1986
The Use of Knowledge in Society (Unknown Binding)
by (shelved 1 time as llm)
avg rating 4.37 — 697 ratings — published 1945
The Pretence of Knowledge (Unknown Binding)
by (shelved 1 time as llm)
avg rating 4.26 — 125 ratings — published 2015
Vision Language Models: Building VLMs with Hugging Face (Paperback)
by (shelved 1 time as llm)
avg rating 0.0 — 0 ratings — published
Building Generative AI Agents: Using LangGraph, AutoGen, and CrewAI (Kindle Edition)
by (shelved 1 time as llm)
avg rating 3.38 — 8 ratings — published
Shipping Machine Learning Systems: A Practical Guide to Building, Deploying, and Scaling in Production (Paperback)
by (shelved 1 time as llm)
avg rating 4.67 — 3 ratings — published
Vibe Coding: Building Production-Grade Software With GenAI, Chat, Agents, and Beyond (Paperback)
by (shelved 1 time as llm)
avg rating 3.78 — 288 ratings — published
深度学习入门：基于Python的理论与实现（图灵图书） (Chinese Edition)
by (shelved 1 time as llm)
avg rating 4.60 — 5 ratings — published
Build a Text-to-Image Generator (from Scratch): With transformers and diffusions
by (shelved 1 time as llm)
avg rating 4.86 — 14 ratings — published
Time Series Forecasting in Python (Paperback)
by (shelved 1 time as llm)
avg rating 4.19 — 31 ratings — published
Practical MLOps: Operationalizing Machine Learning Models (Paperback)
by (shelved 1 time as llm)
avg rating 3.25 — 56 ratings — published
KI Den menneskelige faktor (Hardcover)
by (shelved 1 time as llm)
avg rating 3.18 — 17 ratings — published 2024
Essential GraphRAG: Knowledge Graph-Enhanced RAG (Paperback)
by (shelved 1 time as llm)
avg rating 3.70 — 20 ratings — published
Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents (Kindle Edition)
by (shelved 1 time as llm)
avg rating 4.43 — 14 ratings — published
Deep Reinforcement Learning with Python: With PyTorch, TensorFlow and OpenAI Gym (Kindle Edition)
by (shelved 1 time as llm)
avg rating 4.25 — 4 ratings — published
The RLHF Book: Reinforcement learning from human feedback, alignment, and post-training LLMs (Paperback)
by (shelved 1 time as llm)
avg rating 0.0 — 0 ratings — published
Knowledge Graphs and LLMs in Action: Build AI systems using connected data (Paperback)
by (shelved 1 time as llm)
avg rating 4.50 — 14 ratings — published
Data Analysis with LLMs: Text, tables, images and sound (In Action)
by (shelved 1 time as llm)
avg rating 3.75 — 8 ratings — published
Co-Intelligence: Living and Working with AI (Kindle Edition)
by (shelved 1 time as llm)
avg rating 3.94 — 15,676 ratings — published 2024
“One can, to be sure, program a digital machine in such a way as to be able to carry on a conversation with it, as if with an intelligent partner. The machine will employ, as the need arises, the pronoun “I” and all its grammatical inflections. This, however, is a hoax! The machine will still be closer to a billion chattering parrots—howsoever brilliantly trained the parrots be—than to the simplest, most stupid man. It mimics the behavior of a man on the purely linguistic plane and nothing more.”
― A Perfect Vacuum
― A Perfect Vacuum
“On generative AI, LLMs, etc.
Humans acquire language and communication skills from a diverse range of sources, including raw, unfiltered, and unstructured content. However, when it comes to acquiring knowledge, humans tend to rely on transparent, trusted, and structured sources.
In contrast, ChatGPT and other large language models (LLMs) use a vast array of opaque, unattested sources of raw, unfiltered, and unstructured content as their means of language and communication training and as the source of information used in their responses.
While this approach has proven to be effective in generating natural language, it has also been inconsistent and, at times, significantly lacking in integrity in its responses. While it may provide information, it does not necessarily provide knowledge.
To be truly useful, generative AI must be able to separate language and communication training from the acquisition of knowledge to be used in its responses. This will allow LLMs to not only generate coherent and fluent language but also to provide accurate and reliable information to users. However, in a culture that values self-proclaimed influencers where transparency and accuracy is secondary, it has become increasingly challenging to separate reliable information from misinformation and knowledge from ignorance. This poses a significant obstacle for AI algorithms that strive to provide accurate and trustworthy responses.”
―
Humans acquire language and communication skills from a diverse range of sources, including raw, unfiltered, and unstructured content. However, when it comes to acquiring knowledge, humans tend to rely on transparent, trusted, and structured sources.
In contrast, ChatGPT and other large language models (LLMs) use a vast array of opaque, unattested sources of raw, unfiltered, and unstructured content as their means of language and communication training and as the source of information used in their responses.
While this approach has proven to be effective in generating natural language, it has also been inconsistent and, at times, significantly lacking in integrity in its responses. While it may provide information, it does not necessarily provide knowledge.
To be truly useful, generative AI must be able to separate language and communication training from the acquisition of knowledge to be used in its responses. This will allow LLMs to not only generate coherent and fluent language but also to provide accurate and reliable information to users. However, in a culture that values self-proclaimed influencers where transparency and accuracy is secondary, it has become increasingly challenging to separate reliable information from misinformation and knowledge from ignorance. This poses a significant obstacle for AI algorithms that strive to provide accurate and trustworthy responses.”
―
