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Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques;
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interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand;
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interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand;
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of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research;
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Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries;
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Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key;
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learning approaches used in the diagnosis of different types of cancer along with their research challenges and future directions for the targeted;
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other previously impossible applications. Deep Learning with R is for developers and data scientists with some R experience who;
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sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics;
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Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the;
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research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book;
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research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book;
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Learning, Deep Learning and Intelligent Optimization. Physical devices powered with Artificial Intelligence are gaining importance in diagnosis and;
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Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning;
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Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition;
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learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested;
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learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with;
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Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;
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Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with;
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Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing;
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Learn how to solve challenging machine learning problems with TensorFlow, Google's revolutionary new software library for deep learning. If;
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standard reference on machine learning for economics and finance. This book focuses on economic and financial problems with an empirical dimension;
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detection * Experiment with deep learning concepts, algorithms, and the toolbox for deep learning * Select and split data sets into training, test;
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learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions;
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fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations;
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From humble evolutions in research papers and labs, artificial intelligence (AI)-which encompasses Machine Learning (ML) and Deep Learning;
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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization;
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