{"title":"MACHINE LEARNING","description":null,"products":[{"product_id":"deep-learning-by-ian-goodfellow","title":"Deep Learning   by Ian Goodfellow","description":"\u003cp\u003e\u003cspan class=\"a-text-bold\"\u003eAn introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e“Written by three experts in the field, \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eDeep Learning\u003c\/span\u003e\u003cspan\u003e is the only comprehensive book on the subject.”\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003e—Elon Musk\u003c\/span\u003e\u003cspan\u003e, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"a-text-italic\"\u003eDeep Learning\u003c\/span\u003e\u003cspan\u003e can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.\u003c\/span\u003e\u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099384303727,"sku":null,"price":499.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/A10G_oKN3LL._SY522.jpg?v=1787509740"},{"product_id":"mathematics-for-machine-learning","title":"Mathematics for Machine Learning","description":"\u003cp\u003e\u003cspan\u003eThe fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.\u003c\/span\u003e\u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099407962223,"sku":null,"price":489.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/71ZPgUTDn6L._SY522.jpg?v=1787510527"},{"product_id":"modern-time-series-forecasting-with-python-industry-ready-machine-learning-and-deep-learning-time-series-analysis-with-pytorch-and-pandas","title":"Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas","description":"\u003cp\u003e\u003cspan\u003ePredicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eStarting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThis new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.\u003c\/span\u003e\u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099434471535,"sku":null,"price":547.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/71j6nayIj2L._SY466__2.jpg?v=1787511158"},{"product_id":"hands-on-machine-learning-with-scikit-learn-and-pytorch-concepts-tools-and-techniques-to-build-intelligent-systems-by-aurelien-geron","title":"Hands-On Machine Learning with Scikit-Learn and PyTorch: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron","description":"\u003cp\u003e\u003cspan\u003eThe potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWith an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch—from basic regression techniques to advanced neural networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to build intelligent systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eUnderstand ML basics, including concepts like overfitting and hyperparameter tuning\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eComplete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluation\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLearn techniques for unsupervised learning, such as clustering and anomaly detection\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eBuild advanced architectures like transformers and diffusion models with PyTorch\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eHarness the power of pretrained models—including LLMs—and learn to fine-tune them\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eTrain autonomous agents using reinforcement learning\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099443023983,"sku":null,"price":699.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/81VywRgKFbL._SY522__1.jpg?v=1787511434"},{"product_id":"ai-engineering-building-applications-with-foundation-models-by-chip-huyen","title":"AI Engineering: Building Applications with Foundation Models by Chip Huyen","description":"\u003cp\u003e\u003cspan\u003eRecent breakthroughs in AI have not only increased demand for AI products, they've also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThe book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eAI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications.\u003c\/span\u003e\u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eUnderstand what AI engineering is and how it differs from traditional machine learning engineering\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLearn the process for developing an AI application, the challenges at each step, and approaches to address them\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eExplore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eExamine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eChoose the right model, dataset, evaluation benchmarks, and metrics for your needs\u003c\/span\u003e\u003c\/span\u003e\n\u003cp\u003e\u003cspan\u003eChip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.\u003c\/span\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099447709807,"sku":null,"price":419.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/41otuf9vUuL._SY445_SX342_FMwebp__1.webp?v=1787511556"},{"product_id":"designing-machine-learning-systems-an-iterative-process-for-production-ready-applications-by-chip-huyen","title":"Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen","description":"\u003cp\u003e\u003cspan\u003eMachine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eAuthor Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThis book will help you tackle scenarios such as:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eEngineering data and choosing the right metrics to solve a business problem\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eAutomating the process for continually developing, evaluating, deploying, and updating models\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDeveloping a monitoring system to quickly detect and address issues your models might encounter in production\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eArchitecting an ML platform that serves across use cases\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDeveloping responsible ML systems\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099454165103,"sku":null,"price":399.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/412UGQGrdKL._SY445_SX342_FMwebp__1.webp?v=1787511684"},{"product_id":"reinforcement-learning-second-edition-an-introduction-adaptive-computation-and-machine-learning-series","title":"Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)","description":"\u003cp\u003e\u003cspan class=\"a-text-bold\"\u003eThe significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eReinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eReinforcement Learning\u003c\/span\u003e\u003cspan\u003e, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eLike the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.\u003c\/span\u003e\u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099507576943,"sku":null,"price":599.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/41dDV91Eu3L._SY445_SX342_FMwebp.webp?v=1787512394"},{"product_id":"build-a-reasoning-model-from-scratch","title":"Build a Reasoning Model (From Scratch)","description":"\u003cp\u003e\u003cspan\u003eThis book is a practical guide to understanding how modern reasoning-oriented LLMs work by building their core methods step by step. The book tells a clear engineering story: start with a conventional pre-trained LLM, learn how text generation works, build reliable evaluation tools, improve reasoning through inference-time methods, then move into training-based approaches such as reinforcement learning and distillation.\u003cbr\u003e\u003cbr\u003eThe progression is deliberate. Early chapters establish the baseline model and explain text generation, KV caching, and evaluation with math verifiers. The middle chapters show how reasoning can be improved without changing model weights, using chain-of-thought prompting, sampling, self-consistency, response scoring, and self-refinement. Later chapters move to changing the model itself through reinforcement learning with verifiable rewards, GRPO improvements, format rewards, and finally distillation from stronger reasoning models into smaller ones.\u003cbr\u003e\u003cbr\u003eThe book is especially useful because it implements the core methods from scratch rather than treating them as black-box library calls. Readers see how self-consistency, self-refinement, Best-of-N, and training-based methods actually work, including their cost and latency trade-offs. It also discusses common failure modes, including cases where refinement can make answers worse. Difficult concepts such as softmax, temperature, and top-p sampling are clarified with code-linked explanations and diagrams, and visual workflows make pipelines and scoring methods easier to follow.\u003cbr\u003e\u003cbr\u003eReading the book feels like following a guided technical build rather than a loose survey of AI topics. Each concept is introduced because the project now needs it. Diagrams, roadmaps, code listings, exercises, and repeated workflow summaries help readers stay oriented through advanced material. This structure reflects \u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eSebastian Raschka’s\u003c\/span\u003e\u003cspan\u003e professional strength: explaining complex machine learning topics by making every detail concrete and showing exactly where each section fits in the larger story. He does not treat mechanisms like evaluation, log-probabilities, KL regularization, or distillation as isolated abstractions; he connects them to the goal of making reasoning models understandable and implementable.\u003cbr\u003e\u003cbr\u003ePhysically and organizationally, the book has eight chapters and seven substantial appendixes. That design keeps the main narrative focused while moving supporting material like references, exercise solutions, model source code, larger models, batching, evaluation alternatives, and chat interfaces into ordered appendixes. The result is a logically flowing book that remains hands-on, navigable, and technically deep without constantly interrupting the central build.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eWhat's inside\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e• From-scratch implementations of core LLM reasoning improvements\u003cbr\u003e• Verifier-based evaluation methods\u003cbr\u003e• RL with automatic verifiers for mathematics tasks\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the reader\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003eFor readers who know Python and have some knowledge of machine learning.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the author\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eSebastian Raschka\u003c\/span\u003e\u003cspan\u003e is an LLM Research Engineer with over a decade of experience. He is the author of the bestselling book \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eBuild a Large Language Model (From Scratch).\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eTable of Contents\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e1 Understanding reasoning models\u003cbr\u003e2 Generating text with a pretrained LLM\u003cbr\u003e3 Evaluating reasoning models\u003cbr\u003e4 Improving reasoning with inference-time scaling\u003cbr\u003e5 Inference-time scaling via self-refinement\u003cbr\u003e6 Training reasoning models with reinforcement learning\u003cbr\u003e7 Improving GRPO for reinforcement learning\u003cbr\u003e8 Distilling reasoning models for efficient reasoning\u003cbr\u003eA References and further reading\u003cbr\u003eB Exercise solutions\u003cbr\u003eC Qwen3 LLM source code\u003cbr\u003eD Using larger LLMs\u003cbr\u003eE Batching and throughput-oriented execution\u003cbr\u003eF Common approaches to model evaluation\u003cbr\u003eG Building a chat interface\u003c\/span\u003e\u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099521634415,"sku":null,"price":497.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/71Ahb2iunzL._SY466.jpg?v=1787512500"},{"product_id":"build-a-large-language-model-from-scratch-by-sebastian-raschka","title":"Build a Large Language Model (From Scratch) by Sebastian Raschka","description":"\u003cdiv class=\"a-expander-content a-expander-partial-collapse-content a-expander-content-expanded\"\u003e\n\u003cspan class=\"a-text-italic\"\u003eBuild a Large Language Model (from Scratch)\u003c\/span\u003e\u003cspan\u003e teaches you how to:\u003c\/span\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePlan and code all the parts of an LLM\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePrepare a dataset suitable for LLM training\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eFine-tune LLMs for text classification and with your own data\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eUse human feedback to ensure your LLM follows instructions\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLoad pretrained weights into an LLM\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cspan\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eBuild a Large Language Model (from Scratch)\u003c\/span\u003e\u003cspan\u003e takes you 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.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the technology\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003ePhysicist Richard P. Feynman reportedly said, “I don’t understand anything I can’t build.” Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the book\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eBuild a Large Language Model (From Scratch)\u003c\/span\u003e\u003cspan\u003e is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you’ll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you’ll really understand it because you built it yourself!\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eWhat's inside\u003c\/span\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePlan and code an LLM comparable to GPT-2\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLoad pretrained weights\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eConstruct a complete training pipeline\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eFine-tune your LLM for text classification\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDevelop LLMs that follow human instructions\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cspan\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the reader\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003eReaders need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eAbout the author\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eSebastian Raschka\u003c\/span\u003e\u003cspan\u003e is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software.\u003cbr\u003e\u003cbr\u003eThe technical editor on this book was \u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eDavid Caswell\u003c\/span\u003e\u003cspan\u003e.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eTable of Contents\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003e1 Understanding large language models\u003cbr\u003e2 Working with text data\u003cbr\u003e3 Coding attention mechanisms\u003cbr\u003e4 Implementing a GPT model from scratch to generate text\u003cbr\u003e5 Pretraining on unlabeled data\u003cbr\u003e6 Fine-tuning for classification\u003cbr\u003e7 Fine-tuning to follow instructions\u003cbr\u003eA Introduction to PyTorch\u003cbr\u003eB References and further reading\u003cbr\u003eC Exercise solutions\u003cbr\u003eD Adding bells and whistles to the training loop\u003cbr\u003eE Parameter-efficient fine-tuning with LoRA\u003c\/span\u003e\n\u003c\/div\u003e\n\u003cdiv class=\"a-expander-header a-expander-partial-collapse-header\"\u003e\u003c\/div\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099556663407,"sku":null,"price":490.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/71eljkULtwL._SY466.jpg?v=1787512982"},{"product_id":"introduction-to-algorithms-fourth-edition","title":"Introduction to Algorithms, fourth edition","description":"\u003cp\u003e\u003cspan class=\"a-text-bold\"\u003eA comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics.\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003cbr\u003eSome books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eIntroduction to Algorithms\u003c\/span\u003e\u003cspan\u003e uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, \u003c\/span\u003e\u003cspan class=\"a-text-italic\"\u003eIntroduction to Algorithms\u003c\/span\u003e\u003cspan\u003e has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout.\u003cbr\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan\u003e\u003cu\u003eNew for the fourth edition\u003c\/u\u003e\u003c\/span\u003e\u003cspan\u003e\u003cbr\u003e\u003c\/span\u003e\u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eNew chapters on matchings in bipartite graphs, online algorithms, and machine learning\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003e140 new exercises and 22 new problems\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eReader feedback–informed improvements to old problems\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eClearer, more personal, and gender-neutral writing style\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eColor added to improve visual presentation\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eNotes, bibliography, and index updated to reflect developments in the field\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eWebsite with new supplementary material\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e \u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54149508956271,"sku":null,"price":850.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/61Mw06x2XcL._SY425__1.jpg?v=1787856015"},{"product_id":"computer-systems-a-programmers-perspective","title":"Computer Systems: A Programmer's Perspective","description":"\u003cp\u003e\u003cspan class=\"a-text-bold a-text-italic\"\u003eComputer systems: A Programmer’s Perspective\u003c\/span\u003e\u003cspan\u003e explains the underlying elements common among all computer systems and how they affect general application performance. Written from the programmer’s perspective, this book strives to teach readers how understanding basic elements of computer systems and executing real practice can lead them to create better programs.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eSpanning across computer science themes such as hardware architecture, the operating system, and systems software, the \u003c\/span\u003e\u003cspan class=\"a-text-bold\"\u003eThird Edition\u003c\/span\u003e\u003cspan\u003e serves as a comprehensive introduction to programming. This book strives to create programmers who understand all elements of computer systems and will be able to engage in any application of the field--from fixing faulty software, to writing more capable programs, to avoiding common flaws. It lays the groundwork for readers to delve into more intensive topics such as computer architecture, embedded systems, and cybersecurity.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThis book focuses on systems that execute an x86-64 machine code, and recommends that programmers have access to a Linux system for this course. Programmers should have basic familiarity with C or C++.\u003c\/span\u003e\u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54149511643247,"sku":null,"price":950.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/41i7hOoZLDL._SX342_SY445_FMwebp.webp?v=1787856163"},{"product_id":"code-the-hidden-language-of-computer-hardware-and-software-by-charles-petzold","title":"Code: The Hidden Language of Computer Hardware and Software by Charles Petzold","description":"\u003cp\u003e\u003cspan class=\"a-text-bold\"\u003eThe classic guide to how computers work, updated with new chapters and interactive graphics\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"a-text-italic\"\u003e\"For me, Code was a revelation. It was the first book about programming that spoke to me. It started with a story, and it built up, layer by layer, analogy by analogy, until I understood not just the Code, but the System. Code is a book that is as much about Systems Thinking and abstractions as it is about code and programming. Code teaches us how many unseen layers there are between the computer systems that we as users look at every day and the magical silicon rocks that we infused with lightning and taught to think.\"\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan class=\"a-text-italic\"\u003e- Scott Hanselman, Partner Program Director, Microsoft, and host of Hanselminutes\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eComputers are everywhere, most obviously in our laptops and smartphones, but also our cars, televisions, microwave ovens, alarm clocks, robot vacuum cleaners, and other smart appliances. Have you ever wondered what goes on inside these devices to make our lives easier but occasionally more infuriating?\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eFor more than 20 years, readers have delighted in Charles Petzold's illuminating story of the secret inner life of computers, and now he has revised it for this new age of computing. Cleverly illustrated and easy to understand, this is the book that cracks the mystery. You'll discover what flashlights, black cats, seesaws, and the ride of Paul Revere can teach you about computing, and how human ingenuity and our compulsion to communicate have shaped every electronic device we use.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThis new expanded edition explores more deeply the bit-by-bit and gate-by-gate construction of the heart of every smart device, the central processing unit that combines the simplest of basic operations to perform the most complex of feats. Petzold's companion website, CodeHiddenLanguage.com, uses animated graphics of key circuits in the book to make computers even easier to comprehend.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eIn addition to substantially revised and updated content, new chapters include:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eChapter 18: Let's Build a Clock!\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eChapter 21: The Arithmetic Logic Unit\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eChapter 22: Registers and Busses\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eChapter 23: CPU Control Signals\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eChapter 24: Jumps, Loops, and Calls\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eChapter 28: The World Brain\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003eFrom the simple ticking of clocks to the worldwide hum of the internet, Code reveals the essence of the digital revolution.\u003c\/span\u003e\u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54149513773167,"sku":null,"price":399.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/61Hc-9h_-XL._SY522.jpg?v=1787856314"},{"product_id":"structure-and-interpretation-of-computer-programs-2nd-edition-mit-electrical-engineering-and-computer-science","title":"Structure and Interpretation of Computer Programs - 2nd Edition (MIT Electrical Engineering and Computer Science)","description":"\u003cp\u003e\u003cspan class=\"a-text-italic\"\u003eStructure and Interpretation of Computer Programs\u003c\/span\u003e\u003cspan\u003e has had a dramatic impact on computer science curricula over the past decade. This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.\u003c\/span\u003e\u003c\/p\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54149520785519,"sku":null,"price":499.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/41hjJoVmqoL._SY445_SX342_FMwebp__1.webp?v=1787856444"},{"product_id":"llm-engineers-handbook-master-the-art-of-engineering-large-language-models-from-concept-to-production","title":"LLM Engineer's Handbook: Master the art of engineering large language models from concept to production","description":"\u003cp\u003e\u003cspan\u003eArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThroughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eBy the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch4\u003e\u003cspan\u003eWhat you will learn\u003c\/span\u003e\u003c\/h4\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eImplement robust data pipelines and manage LLM training cycles\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eCreate your own LLM and refine it with the help of hands-on examples\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eGet started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoring\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003ePerform supervised fine-tuning and LLM evaluation\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDeploy end-to-end LLM solutions using AWS and other tools\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDesign scalable and modularLLM systems\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLearn about RAG applications by building a feature and inference pipeline\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003ch4\u003e\u003cspan\u003eWho this book is for\u003c\/span\u003e\u003c\/h4\u003e\n\u003cp\u003e\u003cspan\u003eThis book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios\u003c\/span\u003e\u003c\/p\u003e\n\u003ch4\u003e\u003cspan\u003eTable of Contents\u003c\/span\u003e\u003c\/h4\u003e\n\u003col class=\"a-ordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eUnderstanding the LLM Twin Concept and Architecture\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eTooling and Installation\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eData Engineering\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eRAG Feature Pipeline\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eSupervised Fine-Tuning\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eFine-Tuning with Preference Alignment\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eEvaluating LLMs\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eInference Optimization\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eRAG Inference Pipeline\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eInference Pipeline Deployment\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eMLOps and LLMOps\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ol\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54149527240815,"sku":null,"price":400.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/71C2Wlmta-L._SY466__1.jpg?v=1787856587"},{"product_id":"hands-on-machine-learning-with-scikit-learn-keras-and-tensorflow-concepts-tools-and-techniques-to-build-intelligent-systems-by-aurelien-geron","title":"Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron","description":"\u003cp\u003e\u003cspan\u003eThrough a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eWith this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e \u003c\/p\u003e\n\u003cul class=\"a-unordered-list a-vertical\"\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eUse Scikit-learn to track an example ML project end to end\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eExplore several models, including support vector machines, decision trees, random forests, and ensemble methods\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eExploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eDive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eUse TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54173930061935,"sku":null,"price":699.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/51SLnCjnj-L._SY445_SX342_FMwebp__1.webp?v=1788042176"}],"url":"https:\/\/domainbooksma.com\/collections\/machine-learning.oembed","provider":"Booksdomain","version":"1.0","type":"link"}