Book Image

Deep Learning Essentials

By : Wei Di, Jianing Wei, Anurag Bhardwaj
3 (1)
Book Image

Deep Learning Essentials

3 (1)
By: Wei Di, Jianing Wei, Anurag Bhardwaj

Overview of this book

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as CNN, RNN, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing using Python library such as TensorFlow. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, and small datasets. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.
Table of Contents (12 chapters)

Basics of linear algebra

One of the most fundamental skills required to get oneself setup with deep learning is a foundational understanding of linear algebra. Though linear algebra itself is a vast subject, and covering it in full is outside the scope of this book, we will go through some important aspects of linear algebra in this chapter. Hopefully, this will give you a sufficient understanding of some core concepts and how they interplay with deep learning methodologies.

Data representation

In this section, we will look at core data structures and representations used most commonly across different linear algebra tasks. This is not meant to be a comprehensive list at all but only serves to highlight some of the prominent...