Book Image

Artificial Intelligence with Python

Book Image

Artificial Intelligence with Python

Overview of this book

Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps which intelligently interact with the world around you, building intelligent recommender systems, automatic speech recognition systems and more. Starting with AI basics you'll move on to learn how to develop building blocks using data mining techniques. Discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. This practical book covers a range of topics including predictive analytics and deep learning.
Table of Contents (23 chapters)
Artificial Intelligence with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Types of layers in a CNN


Now that we know about the architecture of a CNN, let's see what type of layers are used to construct it. CNNs typically use the following types of layers:

  • Input layer: This layer takes the raw image data as it is.

  • Convolutional layer: This layer computes the convolutions between the neurons and the various patches in the input. If you need a quick refresher on image convolutions, you can check out this link: http://web.pdx.edu/~jduh/courses/Archive/geog481w07/Students/Ludwig_ImageConvolution.pdf . The convolutional layer basically computes the dot product between the weights and a small patch in the output of the previous layer.

  • Rectified Linear Unit layer: This layer applies an activation function to the output of the previous layer. This function is usually something like max(0, x). This layer is needed to add non-linearity to the network so that it can generalize well to any type of function.

  • Pooling layer: This layer samples the output of the previous layer...