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Advanced Deep Learning with R

You're reading from   Advanced Deep Learning with R Become an expert at designing, building, and improving advanced neural network models using R

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789538779
Length 352 pages
Edition 1st Edition
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Author (1):
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Bharatendra Rai Bharatendra Rai
Author Profile Icon Bharatendra Rai
Bharatendra Rai
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Table of Contents (20) Chapters Close

Preface 1. Section 1: Revisiting Deep Learning Basics FREE CHAPTER
2. Revisiting Deep Learning Architecture and Techniques 3. Section 2: Deep Learning for Prediction and Classification
4. Deep Neural Networks for Multi-Class Classification 5. Deep Neural Networks for Regression 6. Section 3: Deep Learning for Computer Vision
7. Image Classification and Recognition 8. Image Classification Using Convolutional Neural Networks 9. Applying Autoencoder Neural Networks Using Keras 10. Image Classification for Small Data Using Transfer Learning 11. Creating New Images Using Generative Adversarial Networks 12. Section 4: Deep Learning for Natural Language Processing
13. Deep Networks for Text Classification 14. Text Classification Using Recurrent Neural Networks 15. Text classification Using Long Short-Term Memory Network 16. Text Classification Using Convolutional Recurrent Neural Networks 17. Section 5: The Road Ahead
18. Tips, Tricks, and the Road Ahead 19. Other Books You May Enjoy

Revisiting Deep Learning Architecture and Techniques

Deep learning is part of a broader machine learning and artificial intelligence field that uses artificial neural networks. One of the main advantages of deep learning methods is that they help to capture complex relationships and patterns contained in data. When the relationships and patterns are not very complex, traditional machine learning methods may work well. However, with the availability of technologies that help to generate and process more and more unstructured data, such as images, text, and videos, deep learning methods have become increasingly popular as they are almost a default choice to deal with such data. Computer vision and natural language processing (NLP) are two areas that are seeing interesting applications in a wide variety of fields, such as driverless cars, language translation, computer games, and...

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