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Paperback or Softback. Etat : New. R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O and MXNet. Book.
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Taschenbuch. Etat : Neu. R Deep Learning Cookbook | Solve complex neural net problems with TensorFlow, H2O and MXNet | Pks Prakash (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2017 | Packt Publishing | EAN 9781787121089 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]li…bri[dot]de | Anbieter: preigu Print on Demand.
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Powerful, independent recipes to build deep learning models in different application areas using R librariesKey Features:Master intricacies of R deep learning packages such as mxnet & tensorflowLearn application on deep learning in different… domains using practical examples from text, image and speechGuide to set-up deep learning models using CPU and GPUBook Description:Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians.This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance.By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.What You Will Learn:Build deep learning models in different application areas using TensorFlow, H2O, and MXnet.Analyzing a Deep boltzmann machineSetting up and Analysing Deep belief networksBuilding supervised model using various machine learning algorithmsSet up variants of basic convolution functionRepresent data using Autoencoders.Explore generative models available in Deep Learning.Discover sequence modeling using Recurrent netsLearn fundamentals of Reinforcement LeaningLearn the steps involved in applying Deep Learning in text miningExplore application of deep learning in signal processingUtilize Transfer learning for utilizing pre-trained modelTrain a deep learning model on a GPUWho this book is forData science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful.
