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DEEP LEARNING RESOURCES

What is machine learning and AI? What problems can be solved using these approaches? Your journey starts here.
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BINARIZED NEURAL NETWORKS

By PlumerAI for the AI Soc

Explore a novel realm of algorithms optimized for phones and drone microchips. 

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DEEPMIND X UCL DEEP LEARNING SERIES

12 Video tutorial series

The Deep Learning Lecture Series is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence.

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DEEPMIND TEACHING PLATFORM

Curated gallery of DeepMind lessons

Investigate the newly created resource page by Deepmind, targeted at University level students.

explore the ai industry

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AI, GOVERNMENT AND POLICY

Chaired by the AI Soc 2019

Join Ajit Jaokar, David Wood, Tom Charman and Matthew Howard in a panel discussion.

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BenevolentAI: DATA ANALYSIS IN MEDICINE

Introduction to BenevolentAI's ML

The technology uncovers relationship between diseases and symptoms, drugs and their effects, and different underlying causes of diseases amongst patient groups.

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THE ALAN TURING INSTITUTE: AI ETHICS

Debate on Jan 25, 2018

Hear from a panel of experts at the Alan Turing Institute in London discussing AI ethics and the law.

COMmittee recommendations

Have a look at our committee's favourite resources.

January 16th, 2021

Danny's resources & recommendations

1. Reinforcement Learning

UCL David Silver's Course on RL: https://www.davidsilver.uk/teaching/ 

 

2. Deep Learning in general

- MIT 6.S191 2019: 

https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI (MIT Deep-Learning Lectures)

- UCL X Deepmind 2020 - Intro to ML and DL Series:

https://www.youtube.com/watch?v=7R52wiUgxZI&list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF 

3. NLP

by Professor Christopher Manning

- CS 224N: Natural Language Processing with Deep Learning (LINGUIST 284): 

http://web.stanford.edu/class/cs224n/

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Danny Kim, Head of Tutorials

December 23rd, 2020

Sakina's resources & recommendations

1. Mathematical Monk - Machine Learning Playlist (Youtube) https://www.youtube.com/watch?v=yDLKJtOVx5c&list=PLD0F06AA0D2E8FFBA

Short videos covering some of the most important topics in ML, presented in a very digestible way.

2. Caltech - Learning From Data http://www.work.caltech.edu/lectures.html

Excellently taught

3. Tutorial: 21 Fairness Definitions and their Politics - Arvind Narayanan https://www.youtube.com/watch?v=jIXIuYdnyyk

Great intro to fairness

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Sakina Hansen, Journal Club Head

December 10th, 2020

Jess' resources & recommendations

1. An Overview of 11 Proposals for Building Safe Advanced AI
by Evan Hubinger
https://www.alignmentforum.org/posts/fRsjBseRuvRhMPPE5/an-overview-of-11-proposals-for-building-safe-advanced-ai

2. Iterated Amplification
by Paul Christiano
https://www.alignmentforum.org/s/EmDuGeRw749sD3GKd

3. AI Safety via Dabeta
by OpenAI
https://openai.com/blog/debate/

4. Scalable Agent Alignment via Reward Modeling
by Jan Leike
https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via-reward-modeling-bf4ab06dfd84

5. Alignment Newsletter
by Rohin Shah
https://rohinshah.com/alignment-newsletter/

6. Alignment Forum Library
https://www.alignmentforum.org/library

7. AI Alignment
https://ai-alignment.com/

8. Prosaic AI Alignment
by Paul Christiano
https://ai-alignment.com/prosaic-ai-control-b959644d79c2

9. Future of Life Podcast featuring Evan Hubinger
https://futureoflife.org/2020/07/01/evan-hubinger-on-inner-alignment-outer-alignment-and-proposals-for-building-safe-advanced-ai/?cn-reloaded=1

10. Risks from Learned Optimization in Advanced Machine Learning Systems
https://arxiv.org/abs/1906.01820

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Jess James, Corporate Outreach Director

December 7th, 2020

Jianqiao's resources & recommendations

1. Some papers on Google Scholar:

1) The Graph Neural Network Model
2) Understanding the Basis of the Kalman Filter Via a Simple ad Intuitive Derivation
3) Learning Convolutional Neural Networks for Graphs
4) Chinese Sign Language Recognition with Adaptive HMM
5) Chinese Sign Language Recognition Based on Trajectory and Hand Shape Features

2. CS224W: Fundamentals of Graph Neural Networks and GCN

The course introduces from the very beginning the concepts of graphs to advanced graph convolutional neural networks. Very useful and worth watching.

 

Check out this Youtube channel for reference. https://www.youtube.com/playlist?reload=9&list=PL1OaWjIc3zJ4xhom40qFY5jkZfyO5EDOZ

3. Face Recognition with Deep Learning


This article is very interesting and shows how to easily to implement a face recognition system with a few lines of code.


https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78

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Jianqiao Mao, ML Officer