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Siamak Amirghodsi, Meenakshi Rajendran, Shuen Hall Apache Spark 2.x Machine Learning Cookbook Siamak Amirghodsi, Meenakshi Rajendran, Shuen Hall Apache Spark 2.x Machine Learning Cookbook Новинка

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Alex Tellez, Max Pumperla, Michal Malohlava Mastering Machine Learning with Spark 2.x Alex Tellez, Max Pumperla, Michal Malohlava Mastering Machine Learning with Spark 2.x Новинка

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Чехол Air Case для Apple iPhone X/XS, синий, Deppa Чехол Air Case для Apple iPhone X/XS, синий, Deppa Новинка

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Материал: PC (поликарбонат) производства Bayer с покрытием Soft touch Толщина: 1 мм Совместим с 3D стеклами
Jérôme Baton Learning Neo4j 3.x Jérôme Baton Learning Neo4j 3.x Новинка

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Блокнот Turquoise Soft Touch Small, 80 листов Блокнот Turquoise Soft Touch Small, 80 листов Новинка

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Innes Borkwood Learning Stencyl 3.X Game Development. Beginner's Guide Innes Borkwood Learning Stencyl 3.X Game Development. Beginner's Guide Новинка

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Adam Boduch, Jonathan Chaffer, Karl Swedberg Learning jQuery 3.x Adam Boduch, Jonathan Chaffer, Karl Swedberg Learning jQuery 3.x Новинка

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Parag Kulkarni Reinforcement and Systemic Machine Learning for Decision Making Parag Kulkarni Reinforcement and Systemic Machine Learning for Decision Making Новинка

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Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making. Chapters include: Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.
John Slavio Machine Learning for Beginners. An Introduction to Artificial Intelligence and Machine Learning John Slavio Machine Learning for Beginners. An Introduction to Artificial Intelligence and Machine Learning Новинка

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So you've heard a lot about AI and Machine learning. But what does it actually mean?This book could be the answer you're looking for...This book is an introduction to basic machine learning and artificial intelligence. It gives you a list of applications, and also a few examples of the different types of machine learning.Here's What You'll Learn in this Book: Introduction to Machine Learning Different Applications of Machine Learning Introduction to Statistics for Machine Learning Supervised Learning Unsupervised Learning Reinforced Learning Conclusion
Raul Garreta, Guillermo Moncecchi Learning Scikit-Learn. Machine Learning in Python Raul Garreta, Guillermo Moncecchi Learning Scikit-Learn. Machine Learning in Python Новинка

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Reinforcement and Systemic Machine Learning for Decision Making There are always difficulties in making machines that learn from experience. Complete information is not always available—or it becomes available in bits and pieces over a period of time. With respect to systemic learning, there is a need to understand the impact of decisions and actions on a system over that period of time. This book takes a holistic approach to addressing that need and presents a new paradigm—creating new learning applications and, ultimately, more intelligent machines. The first book of its kind in this new and growing field, Reinforcement and Systemic Machine Learning for Decision Making focuses on the specialized research area of machine learning and systemic machine learning. It addresses reinforcement learning and its applications, incremental machine learning, repetitive failure-correction mechanisms, and multiperspective decision making. Chapters include: Introduction to Reinforcement and Systemic Machine Learning Fundamentals of Whole-System, Systemic, and Multiperspective Machine Learning Systemic Machine Learning and Model Inference and Information Integration Adaptive Learning Incremental Learning and Knowledge Representation Knowledge Augmentation: A Machine Learning Perspective Building a Learning System With the potential of this paradigm to become one of the more utilized in its field, professionals in the area of machine and systemic learning will find this book to be a valuable resource.
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