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)

Why Deep Learning?

This chapter will give an overview of deep learning, the history of deep learning, the rise of deep learning, and its recent advances in certain fields. Also, we will talk about challenges, as well as its future potential.

We will answer a few key questions often raised by a practical user of deep learning who may not possess a machine learning background. These questions include:

  • What is artificial intelligence (AI) and deep learning?
  • What’s the history of deep learning or AI?
    • What are the major breakthroughs of deep learning?
    • What is the main reason for its recent rise?
  • What’s the motivation of deep architecture?
    • Why should we resort to deep learning and why can't the existing machine learning algorithms solve the problem at hand?
    • In which fields can it be applied?
    • Successful stories of deep learning
  • What’s the potential future of deep learning and what are the current challenges?