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branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will;
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algorithms for unsupervised data using deep learning concepts with R * Master the common problems faced such as overfitting of data, anomalous;
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Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key Features Get to grips with the;
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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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Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for;
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learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also;
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with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network;
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learning. You'll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later;
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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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-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning;
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Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine;
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. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the;
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Master machine learning techniques with R to deliver insights in complex projects About This Book * Understand and apply machine;
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, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows;
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Behavior Analysis with Machine Learning Using R introduces machine learning and deep learning concepts and algorithms applied to a diverse;
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through deep learning and convolutional neural networks, deep learning with recurrent neural networks, big data with Hadoop (R), Spark (TM) and;
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This book provides readers with a comprehensive and recent exposition in deep learning and its multidisciplinary applications, with a;
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learning with Apache Spark Train neural networks with deep learning libraries such as BigDL and TensorFlow Develop Spark deep learning applications;
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, healthcare, finance, and many more. This book covers deep reinforcement learning using deep-q learning and policy gradient models with coding;
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This book provides a complete illustration of deep learning concepts with case-studies and practical examples useful for real time;
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in neural networks with R, to implement them in your applications * Build smart systems using the power of deep learning * Real-world case studies;
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of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind;
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on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use;
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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 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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fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations;
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