A deep understanding of deep learning (with Python intro)

The theory and math underlying deep learning

How to build artificial neural networks

Architectures of feedforward and convolutional networks

Building models in PyTorch

The calculus and code of gradient descent

Fine-tuning deep network models

Learn Python from scratch (no prior coding experience necessary)

How and why autoencoders work

How to use transfer learning

Improving model performance using regularization

Optimizing weight initializations

Understand image convolution using predefined and learned kernels

Whether deep learning models are understandable or mysterious black-boxes!

Using GPUs for deep learning (much faster than CPUs!)