In this chapter, we covered a lot! We learned how to use dense instead of sparse vectors to represent words, using word2vec or GloVe, although we only used GloVe. We worked with an annotated lexicon; tidy data can already bring a lot of insight! No need to bring in the heavy artillery in many cases. We saw that slightly more complicated models may not perform well (adding layers to the feed-forward neural network); surprisingly, much more complicated models can (using bidirectional LSTMs)! After that, we provided a reference for connecting to Twitter, while keeping in mind that terms of service should be respected. For this, we used previously calculated vector embeddings and models to evaluate the sentiment of new data. And, don't forget, a key point—always check your data! Remember, garbage in, garbage out. Even the best models will provide useless results if the wrong data is used.
R Deep Learning Projects
R Deep Learning Projects
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.
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Current Title:
R Deep Learning Projects
Table of Contents (11 chapters)
Title Page
Packt Upsell
Contributors
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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Index
Customer Reviews