R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O and MXNet
Language: English
Published by Packt Publishing, 2017
- Softcover
- Used
Seller: ThriftBooks-Dallas, Dallas, TX, U.S.A.ThriftBooks-Dallas
AbeBooks seller since July 2, 2009
Condition: Used - Very good
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Add to basketItem description from seller
May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
Seller Inventory # G1787121089I4N00
- Title
- R Deep Learning Cookbook: Solve complex neural net problems with TensorFlow, H2O and MXNet
- Author
- Prakash, Pks; Rao, Achyutuni Sri Krishna
- Publisher
- Packt Publishing
- Publication year
- 2017
- Condition
- Very Good
- Dust jacket
- No Jacket
- Binding
- Paperback
- Language
- English
- ISBN 10
- 1787121089
- ISBN 13
- 9781787121089
- Item weight
- 1.1 pounds
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.
"Synopsis" may belong to another edition of this title.
About the Author
Achyutuni Sri Krishna Rao is a Data Scientist, a Civil Engineer and an Author. He has spent last 4 years in developing many data science solutions to solve problems from leading companies in healthcare, pharmaceutical and manufacturing domain. He is working as Data Science Consultant at ZS Associates. Sri Krishnas background involves a masters in Enterprise Business Analytics and Machine Learning from National University of Singapore, Singapore. His other educational background involves a Bachelors from National Institute of Technology Warangal, India. Sri Krishna has published widely in research areas of civil engineering. He has contributed in a book titled Algorithms and Data Structures using R published by PACKT.
"About the title" may belong to another edition of this title.
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