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http://www.fast.ai https://openreview.net/forum?id=B1xsqj09Fm ( Large Scale GAN Training for High Fidelity Natural Image Synthesis ... Large scale GAN, with very nice positive and negative results)
https://openreview.net/pdf?id=r1Gsk3R9Fm (SHALLOW LEARNING FOR DEEP NETWORKS "A cascade of shallow DNNs [greedy approach] Part of their abstract “Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on two large-scale image recognition tasks: ImageNet and CIFAR-10. Using a simple set of ideas for architecture and training we find that solving sequential 1-hidden-layer auxiliary problems leads to a CNN that exceeds AlexNet performance on ImageNet.”)
https://www.reddit.com/r/MachineLearning/comments/9jtrih/r_large_scale_gan_training_for_high_fidelity/ (reddit discussion also here)
http://www.fast.ai/ ([Start from Random forest in this course])
https://poloclub.github.io/ganlab/ (Visualization) https://zhuanlan.zhihu.com/p/24767059 http://www.sohu.com/a/121189842_465975
https://mxnet.incubator.apache.org
https://cs.stanford.edu/people/karpathy/convnetjs/
[Stock Price] (https://github.com/wagenaartje/stocks.js)
Pure Neural Computing Networks (https://github.com/wagenaartje/neataptic)
Javascript ReInforcement Learning Platform (http://cs.stanford.edu/people/karpathy/reinforcejs/) Books about RL (http://ufal.mff.cuni.cz/~straka/courses/npfl114/2016/sutton-bookdraft2016sep.pdf) Books about RL2 (http://www.aioptify.com/top-reinforcement-learning-books.php)
https://www.quora.com/What-tools-are-good-for-drawing-neural-network-architecture-diagrams