- Paperback: 258 pages
- Publisher: Packt Publishing Limited (22 February 2018)
- Language: English
- ISBN-10: 1788478401
- ISBN-13: 978-1788478403
- Product Dimensions: 19 x 1.5 x 23.5 cm
- Average Customer Review: Be the first to review this item
- Amazon Bestsellers Rank: #1,55,671 in Books (See Top 100 in Books)
R Deep Learning Projects: Master the techniques to design and develop neural network models in R Paperback – Import, 22 Feb 2018
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About the Author
Yuxi (Hayden) Liu is currently an applied research scientist focused on developing machine learning models and systems for given learning tasks. He has worked for a few years as a data scientist, and applied his machine learning expertise in computational advertising. He earned his degree from the University of Toronto, and published five first-authored IEEE transaction and conference papers during his research. His first book, titled Python Machine Learning By Example, was ranked the #1 bestseller in Amazon India in 2017. He is also a machine learning education enthusiast. Pablo Maldonado is an applied mathematician and data scientist with a taste for software development since his days of programming BASIC on a Tandy 1000. As an academic and business consultant, he spends a great deal of his time building applied artificial intelligence solutions for text analytics, sensor and transactional data, and reinforcement learning. Pablo earned his PhD in applied mathematics (with focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France.
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Most helpful customer reviews on Amazon.com
I could use maybe one more word2vec example maybe generating synonyms but that’s a small complaint.
models in R. There are many of R-language users, which have a lot of experience with this popular language for statistical analysis, signal processing, and machine learning.
This book for users who want to use deep learning abilities in their projects but do not know how to integrate it with R-ecosystem. Readers with a strong math background and some experience with R-language will find everything to start their own R deep learning projects.
This book should not be your first book about R or neural networks. You’ll start with the overview of neural networks and deep learning and implement handwritten digit recognition using CNN. A lot of code examples helps you to create your own project related to convolution networks usage. Then you will learn more complex examples like traffic sign recognition, fraud detection, text generation and sentiment analysis. You will learn conceptions of deep convolution neural networks, autoencoders, LSTM and GRU networks, word embeddings, word2vec and GloVe.
You will implement these conceptions with the usage of different packages which can be used for creating of neural networks and deep learning in R: MXNet, H2O, and Keras with TensorFlow.
This book contains a lot of code samples (with downloadable example code files) and it will take you from theory to practice even if you don't yet have hi-level R skills. One important benefit of this book is covering of data pre-processing infrastructure for R. You will not the deep learning conceptions only, but way how to get different datasets (images, texts, series), prepare it for work and preprocess. Generative adversarial networks not covered by this book, but you will receive enough information to implement it yourself using explained R deep learning infrastructure.
Authors combined detailed explanations of theory with real-world examples in this book. It will provide you the ability to integrate deep learning capabilities with strong R-language data analysis infrastructure. I think that R Deep Learning Projects is very useful because it shows deep learning applications for real cases.