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DL_Exploring

A repository of beginner resources to some of the recently developing fields in deep learning that we are exploring, to get a basic understanding of the field.

Please feel free to contribute to this repository by making a pull request!.

Graph Learning

The field mainly focused on applying deep learning on non euclidean data like graphical data using architectures like Graph Convolutional Networks, Graph Neural Networks etc.

  • A Comprehensive Survey on Graph Neural Networks [Link]
  • GRAPH CONVOLUTIONAL NETWORKS - Blog post by Thomas Kipf [Link]
  • PyTorch Geometric - A framework based on pytorch for handling graphical data [Link]
  • DEEP GRAPH LIBRARY - Another popular framework for graphical neural networks [Link]

Meta-Learning

A highly active area of recent research, which aims to learn models which can be easily adpated to learn new tasks often said as "learning to learn".

  • CS 330- Deep Multi-Task and Meta Learning By Chelsea Finn, Stanford [Link]
  • Meta-Learning: Learning to Learn Fast - Blog post in Lil'Log [Link]

Bayesian Deep Learning

Reinforcement Learning

Deals with problems in sequential decision making, has a lot of applications in robotics and simulations.

  • Reinforcement Learning Course by David Silver [Link]
  • CS 285 UC BERKLEY - Deep Reinforcement Learning course by Sergey Levine [Link]
  • Open AI Spinning Up in Deep RL - Good for beginner implementation details [Link]

Active Learning

Deals with problem of expensive data labelling and tries to figure out which unlablled data points to label so as to bring maximum improvement to model performance.

  • Overview of Active Learning for Deep Learning- Highly intuitive blog post for basics of Active Learning [Link]

3D Deep Learning

  • 3D Deep Learning Tutorial [Link]
  • Deep Learning Advances on Different 3D Data Representations: A Survey [Link]
  • Deep Learning for 3D Point Clouds: A Survey [Link]
  • Beyond the pixel plane: sensing and learning in 3D [Link]
  • CS231A: Computer Vision, From 3D Reconstruction to Recognition notes [Link]

Distillation

Pruning

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