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

Experiment with hidden layers and hidden units

The most commonly used layers in neural networks are fully-connected layers. In fully-connected layers, the units in two successive layers are all  connected. However, the units within a layer don't share any connections. As stated before, the connections between the layers are also called trainable parameters. The weights of these connections are trained by the network. The more connections, the more parameters and the more complex patterns can be modeled. Most state-of-the-art models have 100+ million parameters. However, a deep neural network with many layers and units takes more time to train. Also, with extremely deep models the time to infer predictions takes significantly longer (which can be problematic in a real-time environment). In the following chapters, we will introduce other popular layer types that are specific to their network types. 

Picking the correct number of hidden layers and hidden units can be important. When using too...