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)

Summary

In this chapter, we introduced concepts of reinforcement learning and how they are different from traditional supervised learning techniques. We described the core ideas behind RL, as well as basic modules such as Q-learning and policy learning that characterize any reinforcement learning technique today. We also presented deep learning-based advances to traditional RL techniques in form of DRL. We illustrated various different network architectures for DRL and discussed their relative merits. Finally, we sketched the core implementation of a few reinforcement learning tasks as applied to some popular computer-based games.

In next chapter, we will look at some of the practical tips and tricks used while implementing deep learning models in real world applications.