Inside Deep Learning

3 Convolutional neural networks

This chapter covers

• How tensors represent spatial data
• Defining convolutions and their uses
• Building and training a convolutional neural network (CNN)
• Adding pooling to make CNNs more robust
• Augmenting image data to improve accuracy

Convolutional neural networks (CNNs) revitalized the field of neural networks while simultaneously ushering in a new branding of deep learning starting in 2011 and 2012. CNNs are still at the heart of many of the most successful applications of deep learning, including self-driving cars, speech recognition systems used by smart devices, and optical character recognition. All of this stems from the fact that convolutions are powerful yet simple tools that help us encode information ...