Description
Deep learning has revolutionized pattern recognition, introducing tools that power a wide range of technologies in such diverse fields as computer vision, natural language processing, and automatic speech recognition. Applying deep learning requires you to simultaneously understand how to cast a problem, the basic mathematics of modeling, the algorithms for fitting your models to data, and the engineering techniques to implement it all. This book is a comprehensive resource that makes deep learning approachable, while still providing sufficient technical depth to enable engineers, scientists, and students to use deep learning in their own work. No previous background in machine learning or deep learning is required—every concept is explained from scratch and the appendix provides a refresher on the mathematics needed. Runnable code is featured throughout, allowing you to develop your own intuition by putting key ideas into practice.
- Starting from scratch, teaches the concepts and context needed to understand and use deep learning
- Combines the high-quality exposition expected of a textbook with the interactivity of a hands-on tutorial
- Accompanied by a software library with clean runnable code in multiple deep learning frameworks and complemented by an online forum for interactive discussion of technical details and questions that arise
Table of Contents
Installation
Notation
1. Introduction
2. Preliminaries
3. Linear neural networks for regression
4. Linear neural networks for classification
5. Multilayer perceptrons
6. Builders guide
7. Convolutional neural networks
8. Modern convolutional neural networks
9. Recurrent neural networks
10. Modern recurrent neural networks
11. Attention mechanisms and transformers
Appendix. Tools for deep learning
Bibliography
Index.
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