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

Getting started with activation functions

If we only use linear activation functions, a neural network would represent a large collection of linear combinations. However, the power of neural networks lies in their ability to model complex nonlinear behavior. We briefly introduced the non-linear activation functions sigmoid and ReLU in the previous recipes, and there are many more popular nonlinear functions, such as ELULeaky ReLU, TanH, and Maxout.

There is no rule as to activation works best for the units. Deep learning is a new field and most results are obtained by trial and error instead of mathematical proofs. For the output unit, we use a single output unit and a linear activation for regression tasks. For classification tasks with n classes, we use n output nodes and a softmax activation function. The softmax function forces the network to output probabilities between 0 and 1 for mutually exclusive classes and the probabilities sum up to 1. For binary classification, we can...