{"title":"DATA","description":null,"products":[{"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":"designing-data-intensive-applications-the-big-ideas-behind-reliable-scalable-and-maintainable-systems-by-martin-kleppmann","title":"Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann","description":"\u003cp\u003e\u003cspan\u003eData is at the center of many challenges in system design today. Difficult issues such as scalability, consistency, reliability, efficiency, and maintainability need to be resolved. In addition, there's an overwhelming variety of systems, including relational databases, NoSQL datastores, data warehouses, and data lakes. There are cloud services, on-premises services, and embedded databases. What are the right choices for your application? How do you make sense of all these buzzwords?\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eIn this second edition, authors Martin Kleppmann and Chris Riccomini build on the foundation laid in the acclaimed first edition, integrating new technologies and emerging trends. You'll be guided through the maze of decisions and trade-offs involved in building a modern data system, learn how to choose the right tools for your needs, and understand the fundamentals of distributed 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\u003ePeer under the hood of the systems you already use, and learn to use them more effectively\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eMake informed decisions by identifying the strengths and weaknesses of different tools\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eLearn how major cloud services are designed for scalability, fault tolerance, and consistency\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan class=\"a-list-item\"\u003e\u003cspan\u003eUnderstand the core principles upon which modern databases are built\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Booksdomain","offers":[{"title":"Default Title","offer_id":54099463045231,"sku":null,"price":499.0,"currency_code":"MAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0742\/6696\/8175\/files\/51yCnKgSLBL._SY445_SX342_FMwebp.webp?v=1787511751"},{"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":"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\/data.oembed","provider":"Booksdomain","version":"1.0","type":"link"}