PhD Studentship on Artificial Intelligence for Railway Operations and Management
https://phd.leeds.ac.uk/project/504-phd-studentship-on-artificial-inte

AI, Data, Culture: In Conversation with Ben Horowitz, GP, a16z
https://mattturck.com/horowitz/#more-1302

Turing-NLG: the largest language model with 17 billion parameters trained by DeepSpeed
LINKS
Turing-NLP: https://www.microsoft.com/en-us/research/blog/turing-nlg-a-17-billion-parameter-language-model-by-microsoft/
DeepSpeed: https://mspoweruser.com/meet-microsoft-deepspeed-a-new-deep-learning-library-that-can-train-massive-100-billion-parameter-models/
Github: https://github.com/microsoft/DeepSpeed

TRFL : TensorFlow Reinforcement Learning
A library of reinforcement learning building blocks
By DeepMind: https://github.com/deepmind/trfl
#DeepLearning #TensorFlow #ReinforcementLearning

Efficient Graph Generation with Graph Recurrent Attention Networks
Liao et al.: https://arxiv.org/abs/1910.00760
Code: https://github.com/lrjconan/GRAN
#Graph #NeuralNetworks #NeurIPS #NeurIPS2019

Language Models as Knowledge Bases?
Petroni et al.: https://arxiv.org/abs/1909.01066
#Transformers #NaturalLanguageProcessing #MachineLearning

Fine tuning U-Net for ultrasound image segmentation: which layers?. http://arxiv.org/abs/2002.08438

Machine learning identifies variability among children's neural anatomy


http://sciencemission.com/site/index.php

MonoLayout: Amodal scene layout from a single image
Paper: https://arxiv.org/pdf/2002.08394.pdf
Github: https://hbutsuak95.github.io/monolayout/

Introduction to Reinforcement Learning
By DeepMind: https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM- OYHWgPebj2MfCFzFObQ
#DeepLearning #ReinforcementLearning #Robotics

MIT Deep Learning online course *New 2020 Edition* ALL! New lectures every week for the rest of the course with slides, video coding labs
For all lectures, slides, and lab materials http://introtodeeplearning.com/

https://www.youtube.com/watch?v=iaSUYvmCekI&feature=youtu.be

Using neural networks to solve advanced mathematics equations
https://ai.facebook.com/blog/using-neural-networks-to-solve-advanced-mathematics-equations/

Best Practices for Machine Learning engineering

https://github.com/mukeshmithrakumar/Book_List/blob/master/Rules%20of%20Machine%20Learning.pdf

The Global Expansion of AI Surveillance
https://carnegieendowment.org/2019/09/17/global-expansion-of-ai-surveillance-pub-79847

KaoKore: A Pre-modern Japanese Art Facial Expression Dataset
Paper: https://arxiv.org/pdf/2002.08595v1.pdf
Github: https://github.com/rois-codh/kaokore

Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer
we present a large-scale empirical survey to determine which transfer learning techniques work best and apply these insights at scale to create a new model that we call the Text-To-Text Transfer Transformer (T5). We also introduce a new open-source pre-training dataset, called the Colossal Clean Crawled Corpus (C4). The T5 model, pre-trained on C4, achieves state-of-the-art results on many NLP benchmarks while being flexible enough to be fine-tuned to a variety of important downstream tasks. In order for our results to be extended and reproduced, we provide the code and pre-trained models, along with an easy-to-use Colab Notebook to help get started.
https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html