Expires midnight PT on April 19th. Only at manning.com.
Note: Final discount will be applied in cart. For products already on sale, the best available discount will be applied.
Get ready for your next big step with these MEAP and eBook bestsellers. Whether you’re diving into LLMs and AI agents, mastering production-ready Kubernetes workflows, or adapting your models and processes using reinforcement learning, these Manning books will help you bridge the gap between theory and practice.
New MEAP! Bug Bash
Exploratory software testing
Software testers and developers value feedback from real users, but it’s hard to come by until an application is released. A “bug bash” is a time-boxed exploratory testing event designed to find errors, integration issues, and usability problems before a major release. Because a bug bash invites a diverse range of roles and perspectives outside the IT team, it can uncover flaws a structured traditional QA process might miss. And if you do it right, a bug bash is both highly productive and a lot of fun!
Bug Bash: Exploratory software testing shows you how to run a time-boxed testing event with participants from across an organization. In this practical guide, you’ll learn how to recruit the right players, structure the event, and adopt the “game master” skills to keep your bug bash on track. You’ll go step by step through every phase, from getting leadership and dev team buy-in to the post-bash triage and fixes! [Read more]
3 chapters of this new MEAP are available now with more to follow soon!
LLM reasoning models have the power to tackle truly challenging problems that require finding the right path through multiple steps. This book demystifies how these complex models work with a simple philosophy: the best way to learn how something works is to build it yourself! You’ll begin with a pre-trained LLM, adding and improving its reasoning capabilities in ways you can see, test, and understand. [Read more]
AI agent technology is changing fast! This totally revised Second Edition of the bestseller by Micheal Lanham guides you through the latest breakthroughs in large language model (LLM)-powered autonomy, from the Model Context Protocol (MCP) to advanced multi-agent collaboration and deployment. [Read more]
8 chapters of this MEAP are available now, with more to follow soon!
This guide expands the foundational research into judging and adapting AI systems into a collection of practical techniques you can use on the job. As you trace the progression from surface-level text matching, to semantic similarity, to judgment-based evaluation, you’ll build the mental models necessary to choose the right metrics, detect failure modes, and close the loop from evaluation to alignment. [Read more]
3 chapters of this new MEAP are available now with more to follow soon!
A guide to putting LLMs into production in the real world. The book bridges the gap between theory and practice. You’ll go beyond basics like prompting into advanced optimizations: intelligent agents, Retrieval Augmented Generation (RAG), and in-depth solutions for mitigating hallucinations and bias. [Read more]
LLM-powered AI agents are the next leap in applied AI, capable of reasoning and collaboration to achieve even complex, multi-step goals. Using new protocols like MCP and A2A, agents can use software tools, retrieve relevant knowledge, and adapt to feedback. This book guides you step by step in creating an AI agent from the ground up, with clear, detailed explanations you can follow to build your own custom assistants! [Read more]
The definitive guide to developing and deploying software with Kubernetes. Built on the bestselling first edition, this end-to-end revision provides up-to-date coverage on structuring Kubernetes-based applications and taking advantage of the Kubernetes API. You’ll also discover other key topics like configuration, attaching storage, scaling, and zero-downtime maintenance. [Read more]
Go inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you’ll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. [Read more]
Learn the theory behind AI with beautifully simple illustrations, step-by-step pseudocode, and intuitive explanations that make the math simple. You’ll build a clear mental model of how different AI approaches fit together, then implement them with minimal code and maximum insight. [Read more]
This book teaches you to design reliable LLM-powered systems by focusing on the concepts, architectures, and design patterns that will stay stable even as models and APIs change. You’ll learn to structure prompts, compose modular chains, and build RAG pipelines that ingest documents, split them into chunks, embed them, retrieve the right context, and ground answers to eliminate (or vastly reduce) hallucinations. [Read more]
The powerful PyTorch library makes deep learning simple—without sacrificing the features you need to create efficient neural networks, LLMs, and other ML models. Pythonic by design, it’s instantly familiar to users of NumPy, Scikit-learn, and other ML frameworks. This thoroughly-revised second edition covers the latest PyTorch innovations, including how to create and refine generative AI models. [Read more]
After ChatGPT used RLHF to become production-ready, this foundational technique exploded in popularity. In this guide, AI expert Nathan Lambert gives a true industry insider's perspective on modern RLHF training pipelines, and their trade-offs. Using hands-on experiments and mini-implementations, Nathan clearly and concisely introduces the alignment techniques that can transform a generic base model into a human-friendly tool. [Read more]
Reinforcement Learning models develop through trial and error, exploring their environment and learning from successes and mistakes. This powerful AI approach can easily be turned to automatically optimizing business processes like pricing, logistics, and customer engagement. In this guide, you’ll bring RL to solve common yet practical industry challenges. You’ll discover both the algorithms that underpin RL, and how to build the simulation environments you’ll need to train custom models. [Read more]
8 chapters of this MEAP are available now, with more to follow soon!
When you need a language model to respond accurately and quickly about a specific field of knowledge, the sprawling capacity of a LLM may hurt more than it helps. This book teaches you to build generative AI models optimized for specific fields. You’ll learn to minimize the computational horsepower your models require, while keeping high–quality performance times and output. [Read more]
Put the power of deep learning in your hands! This new edition of the bestseller includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python. [Read more]
AI is transforming the world faster than we could have imagined. But how did we get here? AI guru Ilya Sutskever made the bold claim that most of what you need to know about modern AI is captured in 30 seminal research papers on deep learning. In this one-of-a-kind book, 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]
You're receiving this email because you're subscribed to our weekly newsletter. To manage what emails you get, visit our preference center. You may unsubscribe from this promotional mailing list at any time. You will still receive updates on your MEAPs and other Manning books.
Manning Publications, 20 Baldwin Road, PO Box 761, Shelter Island, Suffolk, New York, 11964, US
.jpg?width=460&upscale=true&name=Build%20A%20Reasoning%20Model%20(From%20Scratch).jpg)
.png?width=460&upscale=true&name=Build%20an%20AI%20Agent%20(From%20Scratch).png)
No comments:
Post a Comment