Crash Course in Convolutional Neural Networks for Machine Learning

Last Updated on August 19, 2019

Convolutional Neural Networks are a powerful artificial neural network technique.

These networks preserve the spatial structure of the problem and were developed for object recognition tasks such as handwritten digit recognition. They are popular because people are achieving state-of-the-art results on difficult computer vision and natural language processing tasks.

In this post you will discover Convolutional Neural Networks for deep learning, also called ConvNets or CNNs. After completing this crash course you will know:

• The building blocks used in CNNs such as convolutional layers and pool layers.
• How the building blocks fit together with a short worked example.
• Best practices for configuring CNNs on your own object recognition tasks.
• References for state of the art networks applied to complex machine learning problems.

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.