In this chapter, we will introduce the topic of computer vision and focus on the computer vision state and its applications. By learning to train neural networks with the help of deep learning, we will understand the parallels between the human brain and a neural network by representing the network in a computer system. To optimize our training results, we will also look at effective training techniques and optimization algorithms, which will dramatically decrease the neural network training time, enabling us to have deeper neural networks trained with more data. We will put all of these optimization techniques or parameters together and give a systematic process for accurately choosing their values.
Additionally, we will learn to organize data and the application that we will be creating. At the end of this chapter, we will take a closer look at how a computer perceives vision and images and how to enable a neural network to actually predict many classes.
The chapter will cover the following topics:
- The computer vision state
- Exploring neural networks
- The learning methodology of neural networks
- Organizing data and applications
- Effective training techniques
- Optimizing algorithms
- Configuring the training parameters of the neural network
- Representing images and outputs
- Building a handwritten digit recognizer