{"title":"Yuxi (Hayden) Liu","description":"","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":"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-by-example-fourth-edition-unlock-machine-learning-best-practices-with-real-world-use-cases-paperback","title":"Python Machine Learning By Example - Fourth Edition: Unlock machine learning best practices with real-world use cases - Paperback","description":"\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eAuthor Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling\u003c\/p\u003e\u003cp\u003e- Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions\u003c\/p\u003e\u003cp\u003e- Implement ML models, such as neural networks and linear and logistic regression, from scratch\u003c\/p\u003e\u003cp\u003e- Purchase of the print or Kindle book includes a free PDF copy\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eBook Description: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThe fourth edition of Python Machine Learning by Example is a comprehensive guide for beginners and experienced ML practitioners who want to learn more advanced techniques like multimodal modeling. Written by experienced machine learning author and ex-Google ML engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for ML engineers, data scientists, and analysts.\u003c\/p\u003e\u003cp\u003eExplore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You'll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine.\u003c\/p\u003e\u003cp\u003eThis hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat You Will Learn: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Follow machine learning best practices across data preparation and model development\u003c\/p\u003e\u003cp\u003e- Build and improve image classifiers using Convolutional Neural Networks (CNNs) and transfer learning\u003c\/p\u003e\u003cp\u003e- Develop and fine-tune neural networks using TensorFlow and PyTorch\u003c\/p\u003e\u003cp\u003e- Analyze sequence data and make predictions using RNNs, transformers, and CLIP\u003c\/p\u003e\u003cp\u003e- Build classifiers using SVMs and boost performance with PCA\u003c\/p\u003e\u003cp\u003e- Avoid overfitting using regularization, feature selection, and more\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eThis expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Getting Started with Machine Learning and Python\u003c\/p\u003e\u003cp\u003e- Building a Movie Recommendation Engine\u003c\/p\u003e\u003cp\u003e- Predicting Online Ad Click-Through with Tree-Based Algorithms\u003c\/p\u003e\u003cp\u003e- Predicting Online Ad Click-Through with Logistic Regression\u003c\/p\u003e\u003cp\u003e- Predicting Stock Prices with Regression Algorithms\u003c\/p\u003e\u003cp\u003e- Predicting Stock Prices with Artificial Neural Networks\u003c\/p\u003e\u003cp\u003e- Mining the 20 Newsgroups Dataset with Text Analysis Techniques\u003c\/p\u003e\u003cp\u003e- Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling\u003c\/p\u003e\u003cp\u003e- Recognizing Faces with Support Vector Machine\u003c\/p\u003e\u003cp\u003e- Machine Learning Best Practices\u003c\/p\u003e\u003cp\u003e- Categorizing Images of Clothing with Convolutional Neural Networks\u003c\/p\u003e\u003cp\u003e- Making Predictions with Sequences Using Recurrent Neural Networks\u003c\/p\u003e\u003cp\u003e- Advancing Language Understanding and Generation with Transformer Models\u003c\/p\u003e\u003cp\u003e- Building An Image Search Engine Using Multimodal Models\u003c\/p\u003e\u003cp\u003e- Making Decisions in Complex Environments with Reinforcement Learning\u003c\/p\u003e","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":51319184654559,"sku":"9781835085622","price":66.22,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0813\/8958\/4607\/files\/wSkcvPfjng9781835085622.webp?v=1788366365"}],"url":"https:\/\/blackandbarhe.com\/collections\/yuxi-hayden-liu.oembed","provider":"Black \u0026 Barhe Bookstore","version":"1.0","type":"link"}