Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Chapter 2. Feed-Forward Neural Networks

In this chapter, we will implement Feed-Forward Neural Networks (FNN) and the building blocks for deep learning:

  • Understanding the perceptron
  • Implementing a single-layer neural network
  • Building a multi-layer neural network
  • Getting started with activation functions
  • Hidden layers and hidden units
  • Implementing an autoencoder
  • Tuning the loss function
  • Experimenting with different optimizers
  • Improving generalization with regularization
  • Adding dropout to prevent overfitting