An updated, more cohesive version of this list is available here: Deep Learning, from novice to expert
Tutorials have been chosen to maximize learning curve, i.e. learn the most in the shortest amount of time. Tutorials cover topics from basic deep learning all the way to research done within the last 1 year! They cover significantly more material than a typical deep learning course and take lesser time.
Expected time to completion: 4-6 weeks (20-30 sessions)
Pre-requisites: Calculus (specifically, differentiation)
We'll cover the following topics:
- Machine Learning basics (what it is, gradient descend)
- Deep Learning basics (what it is, neural networks, computational graphs, back-propagation)
- Convolutional Neural Networks (among other applications, CNNs are the most successful architecture for computer vision)
- Recurrent Neural Networks (among other applications, RNNs are the most successful architecture for natural language processing)
- Attention Mechanisms within Neural Networks (Neural Turing Machine, Gated-Recurrent Units, etc)
- Generative Adversarial Networks and Variational Auto-Encoders (mostly applied to image generation)
- Dimensionality reduction (autoencoders, t-SNE)
- Deep Reinforcement Learning
Pro-tip: If you want to skip sections or tutorials, mark them as completed.