theory of deep learning

洋書 Paperback, Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data Analytics Series)Deep Learning and XAI Techniques for Anomaly Detection Integrate the theory and practice of deep anomaly explainability【電子書籍】 Cher SimonFoundations of Deep Reinforcement Learning Theory and Practice in Python【電子書籍】 Laura GraesserLearning Deep Learning Theory and Practice of Neural Networks, Computer Vision, Natural Language Processing, and Transformers Using TensorFlow【電子書籍】 Magnus EkmanApplications of Game Theory in Deep Learning【電子書籍】 Tanmoy HazraAn Intuitive Exploration of Artificial Intelligence Theory and Applications of Deep Learning【電子書籍】 Simant Dube
 

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  • <p><strong>Create interpretable AI models for transparent and explainable anomaly detection with this hands-on guide</strong></p> <p><strong>Purchase of the print or Kindle book includes a free PDF eBook</strong></p> <h4>Key Features</h4> <ul> <li>Build auditable XAI models for replicability and regulatory compliance</li> <li>Derive critical insights from transparent anomaly detection models</li> <li>Strike the right balance between model accuracy and interpretability</li> </ul> <h4>Book Description</h4> <p>Despite promising advances, the opaque nature of deep learning models makes it difficult to interpret them, which is a drawback in terms of their practical deployment and regulatory compliance.</p> <p>Deep Learning and XAI Techniques for Anomaly Detection shows you state-of-the-art methods that'll help you to understand and address these challenges. By leveraging the Explainable AI (XAI) and deep learning techniques described in this ...
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  • <p><strong>The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice</strong></p> <p>Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer gamesーsuch as Go, Atari games, and DotA 2ーto robotics.</p> <p><em><strong>Foundations of Deep Reinforcement Learning</strong></em> is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.</p> <p>This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning c...
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  • <p><strong>NVIDIA's Full-Color Guide to Deep Learning: All You Need to Get Started and Get Results</strong></p> <blockquote> <p>"To enable everyone to be part of this historic revolution requires the democratization of AI knowledge and resources. This book is timely and relevant towards accomplishing these lofty goals."<br /> -- <em>From the foreword by Dr. Anima Anandkumar, Bren Professor, Caltech, and Director of ML Research, NVIDIA</em></p> <p>"Ekman uses a learning technique that in our experience has proven pivotal to successーasking the reader to think about using DL techniques in practice. His straightforward approach is refreshing, and he permits the reader to dream, just a bit, about where DL may yet take us."<br /> -- <em>From the foreword by Dr. Craig Clawson, Director, NVIDIA Deep Learning Institute</em></p> </blockquote> <p>Deep learning (DL) is a key component of today's exciting advances in machine learning and artificial intelligence...
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  • <p>This book aims to unravel the complex tapestry that interweaves strategic decision-making models with the forefront of deep learning techniques. <em>Applications of Game Theory in Deep Learning</em> provides an extensive and insightful exploration of game theory in deep learning, diving deep into both the theoretical foundations and the real-world applications that showcase this intriguing intersection of fields. Starting with the essential foundations for comprehending both game theory and deep learning, delving into the individual significance of each field, the book culminates in a nuanced examination of Game Theory's pivotal role in augmenting and shaping the development of Deep Learning algorithms. By elucidating the theoretical underpinnings and practical applications of this synergistic relationship, we equip the reader with a comprehensive understanding of their combined potential. In our digital age, where algorithms and autonomous agents are becoming more common, ...
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  • <p>This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future.</p> <p><em>An Intuitive Exploration of Artificial Intelligence</em> offers insightful details on how AI works and solves problems in computer vision, natural language understanding, speech understanding, reinforcement learning and synthesis of new content. From the classic problem of recognizing cats and dogs, to building autonomous vehicles, to translating text into another language, to automatically converting speech into text and back to speech, to generating neural art, to playing games, and the author's own experience in building solutions in industry, this book is about explaining how exactly the myriad applications of AI flow out of its immense p...
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