Neumann Optimizer. Nearly 1% validation accuracy on imagenet; can use very large batch sizes

Awesome article. If you do any visualization or computer vision, these are things you need to know (but most people still don’t know!)

You should look at your data. Graphs and charts let you explore and learn about the structure of the information you collect. Good data visualizations also make it easier to communicate your ideas and findings to other people. Beyond that, producing effective plots from your own data is the best way to develop a good eye for reading and understanding graphs—good and bad—made by others, whether presented in research articles, business slide decks, public policy advocacy, or media reports. This book teaches you how to do it.

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. I

Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images

https://figshare.com/s/1a7e25a8229f9636e034

Name and Link Location Organiser Dates Deadline Fee Aid (Travel Grants etc)

Edit face images using a a deep neural network. Users can edit face images using intuitive inputs such as sketching and coloring, from which our network SC-FEGAN generates high quality synthetic images. We used SN-patchGAN discriminator and Unet-like generator with gated convolutional layers.

https://github.com/run-youngjoo/SC-FEGAN

A Guide for Making Black Box Models Explainable.

Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made.

Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games.