Chapter 4. Convolutional Neural Networks
In this chapter, we will talk about CNNs, which are a feather in the cap of deep learning. CNNs have achieved excellent results in many practical applications, particularly in the field of object recognition in images. We will explain and implement the LeNet architecture (LeNet5), which was the first CNN to have great success with the classic MNIST digit classification system. We will also analyze AlexNet, which is a deep CNN that was invented by Alex Krizhevsky. We'll use these networks to introduce transfer learning, which is a machine learning method that utilizes a pre-trained neural network. We will also introduce the VGG architecture, which is usually used as a deep CNN for object recognition. This was developed by Oxford University's renowned Visual Geometry Group (VGG), which performed very well with the ImageNet dataset. This architecture gives us the opportunity to show how to use a neural network to draw a picture in...
