Lectures
You can download the lectures here (in PDF format). I will try to upload lectures prior to their corresponding classes.
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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, pre-training, fine tuning, and visualization.
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Processing Text and Sequential Data
tl;dr: Recurrent models, SimpleRNN, LSTM, GRU, word embeddings, vanishing gradient problem, attention mechanism, and 1D ConvNets
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Advanced Models
tl;dr: Multi input/output models, directed acyclic graphs, weight sharing, callbacks, early-stopping 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, multi-head attention, contextualised embeddings, and BERT
[Slides] [Lecture Video]