Yuxi (Hayden) Liu rozwija modele uczenia maszynowego w Google. Wcześniej pracował naukowo nad zastosowaniami uczenia maszynowego w takich dziedzinach jak reklama internetowa i cyberbezpieczeństwo. Jest entuzjastą edukacji i autorem wielu książek o uczeniu maszynowym. Pierwsze wydanie tego podręcznika zajmowało wiodącą pozycję w rankingu Amazona w latach 2017 i 2018.
Yuxi (Hayden) Liu - książki
Tytuły autora: Yuxi (Hayden) Liu dostępne w księgarni Helion
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To trzecie wydanie popularnego podręcznika, który ułatwi Ci zdobycie praktycznej wiedzy o uczeniu maszynowym w Pythonie. Zapoznasz się z różnymi technikami implementacji algorytmów uczenia maszynowego. Przeanalizujesz rzeczywiste przykłady techniki eksploracyjnej analizy danych, inżynierii cech, klasyfikacji danych, regresji, klastrowania i przetwa- Druk
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Czasowo niedostępna
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Develop your Python machine learning skills with the latest techniques. Dive into NLP transformers, multimodal vision models, and best practices. Explore neural networks, clustering, and GPT with hands-on examples in PyTorch and TensorFlow.
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Fully updated with PyTorch and the latest additions to scikit-learn. Packed with clear explanations, visualizations, and working examples, the book covers essential machine learning techniques in depth, along with two cutting-edge machine learning techniques: transformers and graph neural networks.
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Equipped with the latest updates, this third edition of Python Machine Learning By Example provides a comprehensive course for ML enthusiasts to strengthen their command of ML concepts, techniques, and algorithms.
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This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.
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This Learning Path is your step-by-step guide to building deep learning models using R's wide range of deep learning libraries and frameworks. Through multiple real-world projects and expert guidance and tips, you'll gain the exact knowledge you need to get started with developing deep models using R.
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This book explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations to help you understand the concepts and ideas required to build efficient artificial intelligence systems, this book will help you construct deep models using popular frameworks and datasets.
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Python Machine Learning by Example covers in detail the most important concepts, techniques, algorithms, and libraries that every data scientist or machine learning practitioner needs to know. This example-enriched guide will make your learning journey easier and happier, enabling you to solve real-world data-driven problems.
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R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generat
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Data science and machine learning are some of the top buzzwords in the technical world today. A re-surging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This book is your entry point to machine learning. Through this book, you will learn to apply the concepts of mach
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