Artificial Vision and Language Processing for Robotics

Chapter 5

Convolutional Neural Networks for Computer Vision

Learning Objectives

By the end of this chapter, you will be able to:

• Explain how convolutional neural networks work
• Construct a convolutional neural network
• Improve the constructed model by using data augmentation
• Use state-of-the-art models by implementing transfer learning

In this chapter, we will learn how to use probability distributions as a form of unsupervised learning.

Introduction

In the previous chapter, we learned about how a neural network can be trained to predict values and how a recurrent neural network (RNN), based on its architecture, can prove to be useful in many scenarios. In this chapter, we will discuss and observe how convolutional neural networks (CNNs) ...