Comp541 Deep Learning lecture videos on YouTube

The whole playlist.
Guest lecture by Alan Edelman: Why Julia?
Introduction to Neural Networks
Introduction to Julia
Backpropagation
Softmax Classification
Multi Layer Perceptrons
Convolutional Networks I
Convolutional Networks II
Convolutional Networks III
Recurrent Networks I
Recurrent Networks II
Methodology
Visualization
Optimization
Generalization
Generative Models
Reinforcement Learning: Intro
RL: Markov Decision Processes
RL: Dynamic Programming
RL: Model-free Methods
RL: Value Function Approximation and Policy Gradient Methods


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Knet-the-Julia-dope: An interactive book on deep learning.

Written by Manuel Antonio Morales (@moralesq). This repo is the Julia translation of the mxnet-the-straight-dope repo, a collection of notebooks designed to teach deep learning, MXNet, and the gluon interface. This project grew out of the MIT course 6.338 Modern Numerical Computing with Julia taught by professor Alan Edelman. Our main objectives are:

• Introduce the Julia language and its main packages in the context of deep learning
  
• Introduce Julia's package Knet: an alternative/complementary option to MXNet
  
• Leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place

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Wasserstein GAN: a Julia/Knet implementation

Written by Cem Eteke (@ceteke). This repository contains implementation of WGAN and DCGAN in Julia using Knet. Here is a detailed report about WGAN.
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Relational networks: a Julia/Knet implementation

Written by Erenay Dayanık (@ereday). Knet implementation of "A simple neural network module for relational reasoning" by Santoro et al. (2017). (Relational Networks, arXiv:1706.01427, blog post)
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Neural Style Transfer: a Julia notebook

Written by Cemil Cengiz (@cemilcengiz).

This notebook implements deep CNN based image style transfer algorithm from "Image Style Transfer Using Convolutional Neural Networks" (Gatys et al., CVPR 2016). The proposed technique takes two images as input, i.e. a content image (generally a photograph) and a style image (generally an artwork painting). Then, it produces an output image such that the content(objects in the image) resembles the "content image" whereas the style i.e. the texture is similar to the "style image". In order words, it re-draws the "content image" using the artistic style of the "style image".

The images below show an original photograph followed by two different styles applied by the network.

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