Transformers from scratch
Modern transformers are super simple, so they can be explained in a really straightforward manner
Blog by Peter Bloem, with pytorch code : https://peterbloem.nl/blog/transformers
#MachineLearning #PyTorch #Transformers

"Mathematics for Machine Learning"
Book by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong : https://mml-book.github.io
#ArtificialIntelligence #MachineLearning #Mathematics

Yoshua Bengio :

Happy to share the CFP for the AI for social good workshop at NeurIPS 2019! Please refer to our site for additional details. Workshop site: https://aiforsocialgood.github.io/neurips2019/cfp.htm … Submission site: https://cmt3.research.microsoft.com/NIPSJWAISG2019 Submission deadline: Sept 6

Data Science Roles: A Classification Problem

https://towardsdatascience.com/data-science-roles-a-classification-problem-ebe6fae10169

Spriteworld: A Flexible, Configurable Reinforcement Learning Environment
Watters et al., 2019, DeepMind : https://github.com/deepmind/spriteworld
#deeplearning #artificialintelligence #reinforcementlearning

Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
tl;dr: GANs are simpler to set up than you think
Blog by Dev Nag : https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
#deeplearning #generativeadversarialnetworks #pytorch

Boundless: Generative Adversarial Networks for Image Extension. https://arxiv.org/abs/1908.07007

Joint Embedding of 3D Scan and CAD Objects. https://arxiv.org/abs/1908.06989

Reinforcement Learning Applications. https://arxiv.org/abs/1908.06973

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation... https://arxiv.org/abs/1908.06965

Learning Representations and Agents for Information Retrieval. https://arxiv.org/abs/1908.06132

Robustness package
A library for experimenting with, training and evaluating neural networks, with a focus on adversarial robustness
Engstrom et al. : https://github.com/MadryLab/robustness
#artificialintelligence #deeplearning #neuralnetworks

Generative Adversarial Networks (GANs) in 50 lines of code (PyTorch)
tl;dr: GANs are simpler to set up than you think
Blog by Dev Nag : https://medium.com/@devnag/generative-adversarial-networks-gans-in-50-lines-of-code-pytorch-e81b79659e3f
#deeplearning #generativeadversarialnetworks #pytorch

An Intuitive (and Short) Explanation of Bayes’ Theorem
https://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/

Huawei’s First Commercial AI Chip Doubles the Training Performance of Nvidia’s Flagship GPU

https://syncedreview.com/2019/08/23/huaweis-first-commercial-ai-chip-doubles-the-training-performance-of-nvidias-flagship-gpu/

How Learning Can Guide Evolution
Geoffrey E. Hinton and Steven J. Nowlan : https://www.cs.toronto.edu/~hinton/absps/evolution.htm
#Learning #Evolution

STI – PhD Positions in Machine Learning @ LIONS EPFL (Switzerland)

The LIONS group (https://lions.epfl.ch) at Ecole Polytechnique Federale de Lausanne (EPFL) has several openings for PhD students for research in machine learning and information processing. Please see our research interests.
We are interested in students with EE, CS, and Mathematics backgrounds. Candidates should directly apply to the EDEE or EDIC doctoral programs and list Prof. Volkan Cevher as a potential host for their PhD studies.

More details here: https://www.marktechpost.com/job/sti-phd-positions-in-machine-learning-lions-epfl-switzerland/