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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

Preface

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R and provide real-life examples.

This book will help you apply deep learning algorithms in R using advanced examples. It covers variants of neural network models such as ANN, CNN, RNN, LSTM, and others using expert techniques. In the course of reading this book, you will make use of popular deep learning libraries such as Keras-R, TensorFlow-R, and others to implement AI models.

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