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HALF OFF all print books, including all preorders in MEAP! Renew or get Manning Online Pro annual for just $199.99—SAVE $50!
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Here's your opportunity to catch up on Manning's latest releases, MEAP updates, bestsellers and more!
New MEAP! LLM Customization and Fine-Tuning
LLM Customization and Fine-Tuning is a hands-on playbook for turning a general-purpose open-weights model into a focused, cost-efficient system that’s tailored to your business. You’ll explore the complete adaptation spectrum, from prompting and RAG, through LoRA and QLoRA, to fully supervised fine-tuning, knowledge distillation, and preference alignment with DPO.
Everything you learn is fully reproducible on cost-effective and easy-to-access hardware. You’ll train LoRA and QLoRA adapters on a modest consumer card and perform full fine-tuning on a
single 24 GB card such as an A30 or RTX 4090. All with no requirement for a datacenter. [Read more]
5 chapters of this new MEAP are available now with more to follow soon!
Now in print! Reinforcement Learning from Human Feedback
When ChatGPT proved that Reinforcement Learning from Human Feedback (RLHF) could make AI production-ready, the technique transformed the field overnight. In Reinforcement Learning from Human Feedback, post-training expert Nathan Lambert offers an insider's guide to building AI that is safer, smarter, and aligned with human values. Drawing on his real-world experience building models like Olmo, Lambert shares hard-won insights on preventing over-optimization, crafting model character, and performing rigorous model evaluation.
Through hands-on experiments and mini-
implementations, Lambert walks you through the full post-training pipeline that emerged from RLHF, from foundational theory and landmark papers to the optimization tools that turn a generic base model into a capable AI assistant. You'll explore reward models, instruction tuning, direct alignment algorithms, and the mathematics behind the latest reinforcement learning techniques with LLMs. Then go further with advanced techniques including designing asynchronous systems for reinforcement learning with verifiable rewards (RLVR), constitutional AI, synthetic data, and tool-use. [Read more]
What did Ilya see? Read through Sutskever’s List and you’ll uncover the breakthroughs, ideas, and mental models that shaped the vision of AI guru Ilya Sutskever, all clearly explained and interpreted by veteran author and teacher Richard Heimann.
OpenAI co-founder Ilya Sutskever is one of the most important figures in modern deep learning. Along with developing the GPT models, he is the originator of the famous “Sutskever’s List”—a reading list of 30 research papers that cover “90% of what matters” in AI. In Sutskever’s List,
AI expert Rich Heimann dives into each of these influential and vitally-important papers, revealing the breakthroughs that shaped Ilya’s thinking and the entire AI field. [Read more]
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Secrets of the JavaScript Ninja, Third Edition >> Chapter 12, “Wrangling regular expressions” >> Learn what a JavaScript Ninja knows about regular expressions (including why they rock) as you get a regex refresher, examine complex parsing patterns that regular expressions can’t handle, and take a look at advanced features added by the latest ECMAScript specs.
surface in sequence when you scale AI-assisted development beyond one developer, and discover what the authors mean when they say your codebase is the bottleneck…
how to close the gap between a pile of useful snippets where most data works starts and a pipeline that works across many products, and that you can actually trust. Then take a look at the appendixes, now readily available for your reference, that walk you through setting up your environment, troubleshooting the most common errors, and more. And check out the new focus-setting part openers that prepare you for what you’ll learn in the coming chapters.
through verifying that your LLM works offline (and why that’s more important than you might think), and start building your diagnostic toolkit. Then explore the possibilities for expanding on the foundation you’ve built so far.
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