In this chapter, we introduced different architectures for recurrent neural networks, and pointed out some of their limitations and capabilities. By introducing a naive Markovian model, we compared the efficiency of introducing such complicated architectures. When applied to the text generation problem, we saw that these different architectures had a noticeable improvement in the quality of the predictions. For training networks, we introduced different methods. The classical backpropagation algorithm and other gradient-free methods that are useful to solve black-box optimization problems.
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Book Overview & Buying
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Table Of Contents
R Deep Learning Projects
By :
R Deep Learning Projects
By:
Overview of this book
R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains.
This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects.
By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting.
Table of Contents (7 chapters)
Preface
Free Chapter
Handwritten Digit Recognition Using Convolutional Neural Networks
Traffic Sign Recognition for Intelligent Vehicles
Fraud Detection with Autoencoders
Text Generation Using Recurrent Neural Networks
Sentiment Analysis with Word Embeddings
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