AI & ML

Distributed Deep Learning

Data Parallelism VS Model Parallelism: Link Federated Learning Swarm Learning for decentralized and confidential clinical machine learning – Link AI with swarm intelligence to analyse medical data – Link

Multimodal Deep Learning

MIT 6.S191 Lecture 5 Multimodal Deep Learning – Link What nobody tells you about MULTIMODAL Machine Learning! 🙊 THE definition. – Link Deep Audio-Visual Speech Recognition by Triantafyllos Afouras et. al. – Link

Deep Reinforcement Learning

Deep Reinforcement Learning for Autonomous Driving by Sen Wang et. al. – Link Neural Architecture Search with Reinforcement Learning by Barret Zoph et. al. – Link Deep Reinforcement Learning that Matters by Peter Henderson et. al. – Link https://gym.openai.com/ Reinforcement Learning (DQN) Tutorial – Link A friendly introduction to deep reinforcement learning, Q-networks and policy …

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Contrastive Learning

Contrastive Representation Learning by Lilian Weng – Link Understanding Contrastive Learning (Learn how to learn without labels using self-supervised learning) – Link Supervised Contrastive Learning by Prannay Khosla et. al @NeurIPS 2020 – Link Extending Contrastive Learning to the Supervised Setting by AJ Maschinot et. al. – Link

PyTorch

Pytorch Hooks How to Use PyTorch Hooks by Frank Odom @MediumUnderstanding Hooks by Ayoosh Kathuria @Paperspace Best Practices / Tips & Tricks 7 Tips To Maximize PyTorch Performance by William Falcon @Towardsdatascience

Explainable Neural Networks

Explainable Neural Networks: Recent Advancements, Part 1 Explainable Neural Networks: Recent Advancements, Part 2 Explainable Neural Networks: Recent Advancements, Part 3 Network In Network by Min Lin 2013 Gradient Based Approaches Visualizing Gradients (2014) In the paper “Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps“ Karen Simonyan et. al. proposed two visualisation …

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Interpretable Machine Learning

Books Interpretable Machine Learning: A Guide for Making Black Box Models Explainable by Christoph Molnar Papers Doshi-Velez, Finale, and Been Kim. “Towards a rigorous science of interpretable machine learning” 2017.

Domain Randomization

Domain Randomization: future of robust modeling by Urwa Muaz Invariance, Causality, and Robust Deep Learning – Why do Neural networks fail to generalize to new environments, and how can this be fixed? by by Urwa Muaz