[Source: rdn-consulting]

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• Deep learning 1.0 and beyond by us (Dec, 2020)
• DL for biomedicine by us (June, 2018)
  

• DL series 2020 by DeepMind & UCL
• DL course, Spring 2020, at NYU by Yann LeCun
  
• General: CMU | Montreal (Yoshua Bengio) | Stanford (Andrew Ng) | Paris Saclay | ULiège |
• NLP: Oxford + DeepMind | Standford | 
• Computer Vision: Standford |
• Reinforcement learning: CMU | David Silver | Berkeley | Deep RL Bootcamp |
• Deep unsupervised learning: Berkeley |
• Probabilistic graphical models: Standford (Stefano Ermon) |
• Artificial General Intelligence: Lex Fridman |
• Theory: Stanford (David Donoho) | Montreal (Ioannis Mitliagkas) |

• Google's TensorFlow (Python)
• Facebook's PyTorch (Python)
  
• Amazon's Mxnet (Multiple languages)
  

• Top: RL/planning, DGM, graphs, reasoning
• A list of open problems. 
• Structures (sets & graphs)
• DL for sciences
  
• Theoretical motivations
• Google DeepMind publications
• Dialog systems. Facebook's chatbot.
• Automated programming (aka description2code).
  
• Explainable AI: DARPA
• Creative arts
• Human-machine interacing
  

• Yann LeCun, Eric Horvitz &  Peter Norvig: on Reddit AMA (2018),
• Google Brain: on Reddit AMA (Sept, 2017),
• Yann LeCun: on Quora (July 2016),
• Pedro Domingos: on Quora (Feb 2016),
• Yoshua Bengio: on Quora (Jan 2016),
• Nando de Freitas: on Reddit AMA (Dec, 2015),
• Jürgen Schmidhuber: on Reddit AMA (Feb, 2015)

• Statistical view
• Kernel view, from Poggio's group
• Physics view, renormalization group theory.

• Sebastian Ruder: Multitask, NLP
  
• inFERENCe: GAN

• Trouble shooting neural nets: here, here and here.