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...
