Overview

This section provides a bird's-eye view of what we learned across the book:

• History of artificial intelligence (AI), machine learning—how various improvements in hardware and algorithms triggered huge successes in the implementation of deep learning across different applications. 
• How to use various building blocks of PyTorch, such as variables, tensors, and nn.module, to develop neural networks.
• Understanding the different processes involved in training a neural network, such as the PyTorch dataset for data preparation, data loaders for batching tensors, the torch.nn package for creating network architectures, and using PyTorch loss functions and optimizers. 
• We saw different types of machine learning problems along with challenges, such as overfitting and underfitting. We also went through different techniques, such as data augmentation, adding dropouts, and using batch normalization to prevent overfitting. 
• We learned the different building blocks of Convolution Neural Networks ...