R Deep Learning Cookbook - Prakash, Pks; Rao, Achyutuni Sri Krishna
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Powerful, independent recipes to build deep learning models in different application areas using R libraries Key Features:Master intricacies of R deep learning packages such as mxnet & tensorflow Learn application on deep learning in different domains using practical examples from text, image and speech Guide to set-up deep learning models using CPU and GPU Book 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…mehr

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Produktbeschreibung
Powerful, independent recipes to build deep learning models in different application areas using R libraries Key Features:Master intricacies of R deep learning packages such as mxnet & tensorflow Learn application on deep learning in different domains using practical examples from text, image and speech Guide to set-up deep learning models using CPU and GPU Book 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 machine Setting up and Analysing Deep belief networks Building supervised model using various machine learning algorithms Set up variants of basic convolution function Represent data using Autoencoders. Explore generative models available in Deep Learning. Discover sequence modeling using Recurrent nets Learn fundamentals of Reinforcement Leaning Learn the steps involved in applying Deep Learning in text mining Explore application of deep learning in signal processing Utilize Transfer learning for utilizing pre-trained model Train a deep learning model on a GPU Who this book is for Data 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.
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Autorenporträt
Dr. PKS Prakash is a Data Scientist and an author. He has spent last 12 years in developing many data science solution to solve problems from leading companies in healthcare, manufacturing, pharmaceutical and e-commerce domain. He is working as Data Science Manager at ZS Associates. ZS is one of the world's largest business services firms helping clients with commercial success, by creating data-driven strategies using advanced analytics that they can implement within their sales and marketing operations to make them more competitive, and by helping them deliver impact where it matters. Prakash background involves PhD in Industrial and System Engineering from Wisconsin-Madison, US. He has defended his second PhD in Engineering from University of Warwick, UK. His other educational background involves; Master's from University of Wisconsin-Madison, US and Bachelor's from National Institute of Foundry and Forge Technology (NIFFT), India. He is co-founder of Warwick Analytics which is based on his PhD work from University of Warwick, UK. Prakash has published widely in research areas of operational research & management, soft computing tools and advance algorithms in leading journals such as IEEE-Trans, EJOR, and IJPR among others. He has edited an issue on ""Intelligent Approaches to Complex Systems"" and contributed in books "Evolutionary Computing in Advanced Manufacturing" Published by WILEY and "Algorithms and Data Structures using R" published by PACKT.