{"title":"Sebastian Raschka","description":"Sebastian Raschka has been working on machine learning and AI for more than a decade. Sebastian joined Lightning AI in 2022, where he now focuses on AI and LLM research, developing open-source software, and creating educational material. Prior to that, Sebastian worked at the University of Wisconsin-Madison as an assistant professor in the Department of Statistics, focusing on deep learning and machine learning research. He has a strong passion for education and is best known for his bestselling books on machine learning using open-source software.","products":[{"product_id":"machine-learning-with-pytorch-and-scikit-learn-develop-machine-learning-and-deep-learning-models-with-python-hardcover","title":"Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python - Hardcover","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThis book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free eBook in PDF format.\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eLearn applied machine learning with a solid foundation in theory\u003c\/li\u003e\n\u003cli\u003eClear, intuitive explanations take you deep into the theory and practice of Python machine learning\u003c\/li\u003e\n\u003cli\u003eFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.\u003c\/p\u003e\u003cp\u003ePacked with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.\u003c\/p\u003e\u003cp\u003eWhy PyTorch?\u003c\/p\u003e\u003cp\u003ePyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.\u003c\/p\u003e\u003cp\u003eYou will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).\u003c\/p\u003e\u003cp\u003eThis PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eExplore frameworks, models, and techniques for machines to 'learn' from data\u003c\/li\u003e\n\u003cli\u003eUse scikit-learn for machine learning and PyTorch for deep learning\u003c\/li\u003e\n\u003cli\u003eTrain machine learning classifiers on images, text, and more\u003c\/li\u003e\n\u003cli\u003eBuild and train neural networks, transformers, and boosting algorithms\u003c\/li\u003e\n\u003cli\u003eDiscover best practices for evaluating and tuning models\u003c\/li\u003e\n\u003cli\u003ePredict continuous target outcomes using regression analysis\u003c\/li\u003e\n\u003cli\u003eDig deeper into textual and social media data using sentiment analysis\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.\u003c\/p\u003e\u003cp\u003eBefore you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eGiving Computers the Ability to Learn from Data\u003c\/li\u003e\n\u003cli\u003eTraining Simple Machine Learning Algorithms for Classification\u003c\/li\u003e\n\u003cli\u003eA Tour of Machine Learning Classifiers Using Scikit-Learn\u003c\/li\u003e\n\u003cli\u003eBuilding Good Training Datasets - Data Preprocessing\u003c\/li\u003e\n\u003cli\u003eCompressing Data via Dimensionality Reduction\u003c\/li\u003e\n\u003cli\u003eLearning Best Practices for Model Evaluation and Hyperparameter Tuning\u003c\/li\u003e\n\u003cli\u003eCombining Different Models for Ensemble Learning\u003c\/li\u003e\n\u003cli\u003eApplying Machine Learning to Sentiment Analysis\u003c\/li\u003e\n\u003cli\u003ePredicting Continuous Target Variables with Regression Analysis\u003c\/li\u003e\n\u003cli\u003eWorking with Unlabeled Data - Clustering Analysis\u003c\/li\u003e\n\u003cli\u003eImplementing a Multilayer Artificial Neural Network from Scratch\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319051321567,"sku":"9781837021956","price":115.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/N4j2Ad7k7q9781837021956.webp?v=1788366123"},{"product_id":"python-machine-learning-second-edition-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-paperback","title":"Python Machine Learning - Second Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Paperback","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eUnlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e \u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eSecond edition of the bestselling book on Machine Learning\u003c\/li\u003e \u003cli\u003eA practical approach to key frameworks in data science, machine learning, and deep learning\u003c\/li\u003e \u003cli\u003eUse the most powerful Python libraries to implement machine learning and deep learning\u003c\/li\u003e \u003cli\u003eGet to know the best practices to improve and optimize your machine learning systems and algorithms\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eBook Description\u003c\/strong\u003e\u003cbr\u003e .\u003cbr\u003e Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.\u003cbr\u003e \u003cbr\u003e Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.\u003cbr\u003e \u003cbr\u003e Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library.\u003cbr\u003e \u003cbr\u003e Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities.\u003cbr\u003e \u003cbr\u003e If you've read the first edition of this book, you'll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.\u003c\/p\u003e \u003cp\u003e\u003cstrong\u003eWhat You Will Learn\u003c\/strong\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eUnderstand the key frameworks in data science, machine learning, and deep learning\u003c\/li\u003e \u003cli\u003eHarness the power of the latest Python open source libraries in machine learning\u003c\/li\u003e \u003cli\u003eExplore machine learning techniques using challenging real-world data\u003c\/li\u003e \u003cli\u003eMaster deep neural network implementation using the TensorFlow 1.x library\u003c\/li\u003e \u003cli\u003eLearn the mechanics of classification algorithms to implement the best tool for the job\u003c\/li\u003e \u003cli\u003ePredict continuous target outcomes using regression analysis\u003c\/li\u003e \u003cli\u003eUncover hidden patterns and structures in data with clustering\u003c\/li\u003e \u003cli\u003eDelve deeper into textual and social media data using sentiment analysis\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003cstrong\u003eWho this book is for\u003c\/strong\u003e\u003c\/p\u003e \u003cp\u003eIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential and unmissable resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for developers and data scientists who want to teach computers how to learn from data.\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319066656991,"sku":"9781787125933","price":66.22,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/H13NzPaLF29781787125933.webp?v=1788366152"},{"product_id":"machine-learning-with-pytorch-and-scikit-learn-develop-machine-learning-and-deep-learning-models-with-python-paperback","title":"Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python - Paperback","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eThis book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free eBook in PDF format.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Learn applied machine learning with a solid foundation in theory\u003c\/p\u003e\u003cp\u003e- Clear, intuitive explanations take you deep into the theory and practice of Python machine learning\u003c\/p\u003e\u003cp\u003e- Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems.\u003c\/p\u003e\u003cp\u003ePacked with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself.\u003c\/p\u003e\u003cp\u003eWhy PyTorch?\u003c\/p\u003e\u003cp\u003ePyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric.\u003c\/p\u003e\u003cp\u003eYou will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP).\u003c\/p\u003e\u003cp\u003eThis PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Explore frameworks, models, and techniques for machines to learn from data\u003c\/p\u003e\u003cp\u003e- Use scikit-learn for machine learning and PyTorch for deep learning\u003c\/p\u003e\u003cp\u003e- Train machine learning classifiers on images, text, and more\u003c\/p\u003e\u003cp\u003e- Build and train neural networks, transformers, and boosting algorithms\u003c\/p\u003e\u003cp\u003e- Discover best practices for evaluating and tuning models\u003c\/p\u003e\u003cp\u003e- Predict continuous target outcomes using regression analysis\u003c\/p\u003e\u003cp\u003e- Dig deeper into textual and social media data using sentiment analysis\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch.\u003c\/p\u003e\u003cp\u003eBefore you get started with this book, you'll need a good understanding of calculus, as well as linear algebra.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Giving Computers the Ability to Learn from Data\u003c\/p\u003e\u003cp\u003e- Training Simple Machine Learning Algorithms for Classification\u003c\/p\u003e\u003cp\u003e- A Tour of Machine Learning Classifiers Using Scikit-Learn\u003c\/p\u003e\u003cp\u003e- Building Good Training Datasets - Data Preprocessing\u003c\/p\u003e\u003cp\u003e- Compressing Data via Dimensionality Reduction\u003c\/p\u003e\u003cp\u003e- Learning Best Practices for Model Evaluation and Hyperparameter Tuning\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e(N.B. Please use the Read Sample option to see further chapters)\u003c\/strong\u003e\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319087431903,"sku":"9781801819312","price":79.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/Dxf4Z3M-FW9781801819312.webp?v=1788366187"},{"product_id":"python-machine-learning-machine-learning-and-deep-learning-with-python-scikit-learn-and-tensorflow-2-paperback","title":"Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Paperback","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eApplied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free eBook in the PDF format.\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eThird edition of the bestselling, widely acclaimed Python machine learning book\u003c\/li\u003e\n\u003cli\u003eClear and intuitive explanations take you deep into the theory and practice of Python machine learning\u003c\/li\u003e\n\u003cli\u003eFully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003ePython Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.\u003c\/p\u003e\u003cp\u003ePacked with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.\u003c\/p\u003e\u003cp\u003eUpdated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.\u003c\/p\u003e\u003cp\u003eThis book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eMaster the frameworks, models, and techniques that enable machines to 'learn' from data\u003c\/li\u003e\n\u003cli\u003eUse scikit-learn for machine learning and TensorFlow for deep learning\u003c\/li\u003e\n\u003cli\u003eApply machine learning to image classification, sentiment analysis, intelligent web applications, and more\u003c\/li\u003e\n\u003cli\u003eBuild and train neural networks, GANs, and other models\u003c\/li\u003e\n\u003cli\u003eDiscover best practices for evaluating and tuning models\u003c\/li\u003e\n\u003cli\u003ePredict continuous target outcomes using regression analysis\u003c\/li\u003e\n\u003cli\u003eDig deeper into textual and social media data using sentiment analysis\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eGiving Computers the Ability to Learn from Data\u003c\/li\u003e\n\u003cli\u003eTraining Simple Machine Learning Algorithms for Classification\u003c\/li\u003e\n\u003cli\u003eA Tour of Machine Learning Classifiers Using scikit-learn\u003c\/li\u003e\n\u003cli\u003eBuilding Good Training Datasets - Data Preprocessing\u003c\/li\u003e\n\u003cli\u003eCompressing Data via Dimensionality Reduction\u003c\/li\u003e\n\u003cli\u003eLearning Best Practices for Model Evaluation and Hyperparameter Tuning\u003c\/li\u003e\n\u003cli\u003eCombining Different Models for Ensemble Learning\u003c\/li\u003e\n\u003cli\u003eApplying Machine Learning to Sentiment Analysis\u003c\/li\u003e\n\u003cli\u003eEmbedding a Machine Learning Model into a Web Application\u003c\/li\u003e\n\u003cli\u003ePredicting Continuous Target Variables with Regression Analysis\u003c\/li\u003e\n\u003cli\u003eWorking with Unlabeled Data - Clustering Analysis\u003c\/li\u003e\n\u003cli\u003eImplementing a Multilayer Artificial Neural Network from Scratch\u003c\/li\u003e\n\u003cli\u003eParallelizing Neural Network Training with TensorFlow\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319161979103,"sku":"9781789955750","price":79.18,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/bKcB6eaq639781789955750.webp?v=1788366322"},{"product_id":"build-a-reasoning-model-from-scratch-paperback","title":"Build a Reasoning Model (from Scratch) - Paperback","description":"\u003cp\u003e\u003cb\u003eGet the eBook free when you register your print book at Manning.\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\"An exceptional deep dive into the next frontier of AI.\"\u003cbr\u003e --Aman Chadha, Google \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\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 \u003cb\u003eSebastian Raschka's\u003c\/b\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. \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e - From-scratch implementations of core LLM reasoning improvements\u003cbr\u003e - Verifier-based evaluation methods\u003cbr\u003e - RL with automatic verifiers for mathematics tasks \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For readers who know Python and have some knowledge of machine learning. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eSebastian Raschka\u003c\/b\u003e is an LLM Research Engineer with over a decade of experience. He is the author of the bestselling book \u003ci\u003eBuild a Large Language Model (From Scratch).\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 1 Understanding reasoning models\u003cbr\u003e 2 Generating text with a pretrained LLM\u003cbr\u003e 3 Evaluating reasoning models\u003cbr\u003e 4 Improving reasoning with inference-time scaling\u003cbr\u003e 5 Inference-time scaling via self-refinement\u003cbr\u003e 6 Training reasoning models with reinforcement learning\u003cbr\u003e 7 Improving GRPO for reinforcement learning\u003cbr\u003e 8 Distilling reasoning models for efficient reasoning\u003cbr\u003e A References and further reading\u003cbr\u003e B Exercise solutions\u003cbr\u003e C Qwen3 LLM source code\u003cbr\u003e D Using larger LLMs\u003cbr\u003e E Batching and throughput-oriented execution\u003cbr\u003e F Common approaches to model evaluation\u003cbr\u003e G Building a chat interface","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319169351903,"sku":"9781633434677","price":86.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/U2vlZghWPr9781633434677.webp?v=1788366337"}],"url":"https:\/\/blackandbarhe.com\/collections\/sebastian-raschka.oembed","provider":"Black \u0026 Barhe Bookstore","version":"1.0","type":"link"}