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

Understanding padding and strides

Up until now, we've used the default strides of one for our networks. This indicates that the model convolves one input over each axis (step size of one). However, when a dataset contains less granular information on the pixel level, we can experiment with larger values as strides. By increasing the strides, the convolutional layer skips more input variables over each axis, and therefore the number of trainable parameters is reduced. This can speed up convergence without too much performance loss.

Another that can be tuned is the padding. The padding defines how the borders of the input data (for example images) are handled. If no padding is added, only the border pixels (in the case of an image) will be included. So if you expect the borders to include valuable information, you can try to add padding to your data. This adds a border of dummy data that can be used while convolving over the data. A benefit of using padding is that the dimensions of the data...