Deep CNN Architectures
In this chapter, we will first briefly review the evolution of Convolutional Neural Network (CNN) architectures, and then we will study the different CNN architectures in detail. We will implement these CNN architectures using PyTorch, and in doing so, we aim to exhaustively explore the tools (modules and built-in functions) that PyTorch has to offer in the context of building Deep CNNs. Gaining strong CNN expertise in PyTorch will enable us to solve a number of deep learning problems involving CNNs. This will also help us in building more complex deep learning models or applications of which CNNs are a part.
This chapter will cover the following topics:
- Why are CNNs so powerful?
- Evolution of CNN architectures
- Developing LeNet from scratch
- Fine-tuning the AlexNet model
- Running a pretrained VGG model
- Exploring GoogLeNet and Inception v3
- Discussing ResNet and DenseNet architectures
- Understanding EfficientNets and the future of CNN architectures
All the code files for this chapter can be found at https://github.com/arj7192/MasteringPyTorchV2/tree/main/Chapter03.
Let us start by discussing the key features of CNNs.
