10 Best-Selling AI Models Books Millions Love

When Frank Hutter and his co-authors set out to write this book, they recognized the growing need to simplify machine learning for users without deep expertise. The book methodically walks you through the core methods underpinning automated machine learning, detailing how systems can select models and tune parameters independently. You’ll find clear discussions on optimization techniques and real examples from international AutoML challenges, giving you insight into how automation is reshaping AI development. This text suits you especially well if you’re a researcher or practitioner eager to apply AutoML concepts without getting lost in complex manual tuning.

Brian D. Ripley’s decades as a Professor of Applied Statistics at Oxford shaped this deep exploration of pattern recognition. You’ll find a blend of statistical decision theory and neural network approaches that ground the subject firmly in probability and statistics. The book walks you through practical examples—like decision trees and belief networks—that shed light on how these models tackle real-world classification problems. If you have a solid grasp of statistics and some calculus, you’ll gain a clearer perspective on machine learning’s theoretical foundations and applications. This book suits those wanting a rigorous, mathematically driven understanding rather than a beginner’s overview.

This tailored book explores proven automated machine learning (AutoML) techniques carefully matched to your background and goals. It covers core concepts like model selection, hyperparameter tuning, and performance optimization, while delving into advanced AutoML workflows that align with the complexities you want to address. By focusing on your specific interests and experience level, it reveals how to apply battle-tested methods effectively, avoiding unnecessary breadth and honing in on what truly matters for your projects. The personalized approach ensures you engage deeply with AutoML concepts that resonate with your challenges, encouraging a richer understanding and practical skill development.

After more than a decade bridging academia and industry, Sebastian Raschka offers a hands-on journey through building large language models entirely from scratch. You’ll learn to design, code, pretrain, and fine-tune your own LLM, gaining insight into attention mechanisms, dataset preparation, and human feedback integration. The book breaks down complex topics like GPT-style architectures into manageable steps, revealing the inner workings of generative AI with clear examples and diagrams. It suits anyone with intermediate Python and machine learning knowledge aiming to understand and customize LLMs on their own laptop, rather than relying on prebuilt libraries.

Unlike most AI books that skim over implementation details, this one dives straight into using R to build neural networks, guided by Giuseppe Ciaburro's expertise in AI and deep learning. You'll learn how to set up R packages, understand neurons, perceptrons, activation functions, and master both recurrent and convolutional neural networks. The book offers practical examples and case studies that clarify complex concepts, making it ideal if you have a statistical background and want to apply neural networks to real-world problems. If you're eager to deepen your programming skills in AI models and handle complex data, this book gives you the tools and insights to do so confidently.

Charu C. Aggarwal’s decades of research experience at IBM culminate in this detailed exploration of neural networks and deep learning. You’ll gain a thorough understanding of why neural networks perform as they do, when depth matters, and the challenges in training these models. The book methodically bridges traditional machine learning methods with modern neural architectures, covering algorithms like support vector machines and word2vec as special cases. It also traverses advanced topics such as convolutional and recurrent networks, alongside practical applications in areas like recommender systems and image classification. This text suits graduate students, researchers, and practitioners ready to deepen their grasp of neural network theory and application.

This tailored book explores the step-by-step process of large language model (LLM) development, focusing on rapid results tailored to your background and goals. It combines widely validated knowledge from millions of readers with your specific interests to deliver a clear, engaging path through LLM fine-tuning, training techniques, and deployment considerations. By customizing content to match your experience level and learning objectives, the book reveals how to navigate the complex landscape of LLMs efficiently, emphasizing practical understanding of model architectures, datasets, and optimization methods. This personalized approach enables you to focus deeply on what matters most for your rapid progress in building and refining large language models.

What happens when a tech enthusiast turns educator to tackle generative AI? Nicholas Dempsey offers a clear-eyed guide that unpacks complex topics like neural networks and deep learning without drowning you in jargon. You’ll explore practical ways to create AI-generated art, music, and text, while also learning strategies to monetize these skills in the digital economy. This book suits students, entrepreneurs, or anyone curious about applying AI creatively and commercially. Chapters include hands-on exercises that reinforce concepts, making it a solid introduction rather than a superficial overview.

What started as a mission to demystify large language models (LLMs) evolved into a thorough manual crafted by Paul Iusztin and Maxime Labonne, who bring years of hands-on MLOps and AI engineering experience. You’ll learn to build, fine-tune, and deploy LLMs beyond simple prototypes, moving into scalable, production-ready systems incorporating data pipelines, supervised fine-tuning, and inference optimization. The book’s detailed chapters, like those on RAG feature pipelines and preference alignment, equip you with practical skills to navigate LLMOps complexities. If you’re an AI engineer or NLP professional aiming to transition from theory to real-world LLM applications, this book is a solid guide tailored to that journey.

What started as a passion project by Louis-François Bouchard to demystify AI evolved into a detailed manual for developers eager to harness Large Language Models (LLMs) in real-world applications. You’ll learn not just the theory behind LLMs but practical skills like prompting, fine-tuning, and implementing Retrieval-Augmented Generation (RAG) with frameworks such as LangChain and LlamaIndex. The book walks you through creating scalable, reliable AI products with hands-on code examples and accessible explanations, making it ideal if you have intermediate Python experience and want to deepen your AI engineering toolkit. However, if you're entirely new to programming, some sections might feel challenging without prior coding knowledge.

Unlike many AI-focused books that dwell on theory, this one delivers a hands-on approach grounded in the expertise of Jay Alammar, whose visual explanations have already guided millions through complex machine learning ideas. You gain clear insights into Transformer architectures, semantic search beyond keywords, and how to fine-tune large language models for specific tasks like copywriting or summarization. Chapters on building pipelines for clustering text and retrieval-augmented generation equip you with practical skills rather than abstract concepts. This book suits developers and data scientists eager to apply large language models directly, though those seeking purely theoretical discussions might find it less appealing.

What happens when a seasoned data scientist with deep expertise in AI and big data tackles generative models? Joseph Babcock distills years of work in e-commerce, streaming, and quantitative finance into a hands-on exploration of creative AI. You’ll dive into building and adapting models like VAEs, GANs, LSTMs, and transformers with TensorFlow 2, gaining practical skills such as music composition with MuseGAN and text generation pipelines using GPT-2. This book suits Python programmers who want to experiment with generative AI’s creative potential, offering code examples and research insights without overwhelming theory.