Deep Learning Foundations

This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning.

1. Front Matter

2. Introduction

3. Supervised Learning

4. Unsupervised Learning

5. Ensemble Learning

6. Deep KNN Algorithm

7. Deep Probabilistic Learning

8. Deep Probabilistic Learning

9. Deep Linear Classifier

10. Multiple Layer Perceptron

11. Recurrent Neural Networks

12. Restricted Boltzmann Machine

13. Convolutional Neural Networks

14. Index Expansion

15. Text Summarization

16. Textual Deep Operations

17. Text Classification System