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)

What is reinforcement learning (RL)?

So far in this book, we have looked at AI as a framework, to learn from vast amounts of data. For instance, if you are training an image classifier such as MNIST, you need labels for each image as to what digit they represent. Alternatively, if you are training a machine translation system, you need to provide a parallel aligned corpus of pairwise sentences, where each pair constitutes a sentence in a source language and an equivalent translation in a target language. Given such settings, it is possible to build an efficient deep learning-based AI system today.

However, one of the core challenges that still remains for mass-scale deployment and industrialization of such systems is the requirement of high-quality labeled data. Obtaining data is cheap, but curating and annotating is expensive as it requires manual intervention. One of the grand...