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?

Mathematical Building Blocks of Neural Networks
tl;dr: What are building blocks of networks, i.e. tensors? How to train a network with gradient descent and backpropagation?

Getting Started with Neural Networks
tl;dr: Getting a taste of deep learning with three examples: sentiment analysis, text classification, and regression.

Fundamentals of Machine Learning
tl;dr: A general overview of ML, its branches, data representation, feature engineering, overfitting and regularization.
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Computer Vision
tl;dr: A review of ConvNets, data augmentation, pretraining, 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 ID ConvNets
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Advanced Models
tl;dr: Multi input/output models, directed acyclic graphs, weight sharing, callbacks, earlystopping and checkpointing, and Tensorboard.
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Generative Deep Learning
tl;dr: Text generation with LSTMs, DeepDream, Neural Style Transfer, concept space, variational autoencoders (VAE), generative adversarial networks (GAN)
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TensorFlow 2 Tutorial
tl;dr: A set of tutorials, prepared by IUST, on TensorFlow 2 (basically a minicourse): It covers most of the required materials to start coding in TensorFlow, such as defining the computation, designing the model, working the input pipeline, and many more.
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