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Advanced Deep Learning with R

You're reading from  Advanced Deep Learning with R

Product type Book
Published in Dec 2019
Publisher Packt
ISBN-13 9781789538779
Pages 352 pages
Edition 1st Edition
Languages
Author (1):
Bharatendra Rai Bharatendra Rai
Profile icon Bharatendra Rai

Table of Contents (20) Chapters

Preface 1. Section 1: Revisiting Deep Learning Basics
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

Summary

Deep learning methods that make use of artificial neural networks have been increasing in popularity in recent years. A number of areas of application involving deep learning methods include driverless cars, image classification, natural language processing, and new image generation. We started this first chapter by looking at the popularity of the deep learning term as reported from a Google trend website. We described a general five-step process for applying deep learning methods and developed some broad ideas about details within each step. We then briefly looked at deep learning techniques covered in each chapter and situations in which they are applied, along with some best practices.

In the next chapter, we get started with an application example and illustrate steps for developing a deep network model for multi-class classification problems.

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