Lectures
You can download the lectures here (in PDF format). I will try to upload lectures prior to their corresponding classes.

Introduction to Deep Learning
tl;dr: What is deep learning? How is it different from machine learning? What is data representation?
[Slides] 
Machine Learning Background
tl;dr: Supervised learning, regression, splitting data, overfitting, normalization, regularization and dropout.
[Slides] 
Fundamentals of Neural Networks
tl;dr: What are building blocks of networks, i.e. tensors? How to train a network with gradient descent and backpropagation?
[Slides] [Lecture Video] 
Getting Started With Neural Networks
tl;dr: Getting a taste of deep learning with three examples: sentiment analysis, text classification, and regression.
[Slides] [Lecture Video] 
Computer Vision
tl;dr: A review of ConvNets, data augmentation, pretraining, fine tuning, and visualization.

Processing Text and Sequential Data
tl;dr: Recurrent models, SimpleRNN, LSTM, GRU, word embeddings, vanishing gradient problem, attention mechanism, and 1D ConvNets

Advanced Models
tl;dr: Multi input/output models, directed acyclic graphs, weight sharing, callbacks, earlystopping and checkpointing, and Tensorboard.
[Slides] 
Generative Deep Learning
tl;dr: Text generation with LSTMs, DeepDream, Neural Style Transfer, concept space, variational autoencoders (VAE), generative adversarial networks (GAN)
[Slides] 
Transformer and BERT
tl;dr: The Transformer architecure for sequence encoding, multihead attention, contextualised embeddings, and BERT
[Slides] [Lecture Video]