Sharing some of the latest research, announcements, and resources on deep learning. By Isaac Madan ( email ) Continuing our series of deep learning updates, we pulled together some of the awesome resources that have emerged since our last post. In case you missed it, here are our past updates: , , , , , , , , , , , ( , , and the of 20+ resources we outlined in April 2016. As always, this list is not comprehensive, so if there’s something we should add, or if you’re interested in discussing this area further. August July June ( part 1 , part 2 , part 3 , part 4 ) May April ( part 1 , part 2 ) March part 1 February November September part 2 & October part 1 September part 1 August ( part 1 , part 2 ) July part 1 part 2 ), June original set let us know Research & Announcements by Hitaj of Stevens Institute of Technology. Using a GAN to guess LinkedIn passwords based on a corpus of previously leaked passwords — they were able to crack 27% of the 143 million leaked passwords in their data set when combined with a traditional password guessing tool HashCat. PassGAN: A Deep Learning Approach for Password Guessing et al by Adrian Bulat and Georgios Tzimiropoulos of the University of Nottingham. A deep-dive into the application of deep learning to face alignment, or facial landmark localization — meaning identifying the geometric structure of faces in images. Original paper here. How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) by Naftali Tishby. Explanation of the , as an explanation as to why deep learning works. Youtube video . Information Theory of Deep Learning information bottleneck Tishby argues that deep neural networks learn according to a procedure called the “information bottleneck,” which he and two collaborators first described in purely theoretical terms in 1999. The idea is that a network rids noisy input data of extraneous details as if by squeezing the information through a bottleneck, retaining only the features most relevant to general concepts. here by Kosinski of Stanford. Deep neural networks are more accurate than humans at detecting sexual orientation from facial images et al In a controversial new study that is setting the internet ablaze, researchers claim that artificial intelligence can be used to accurately detect someone’s sexual orientation. The research utilized deep neural networks to examine over 35,000 publicly-available dating site photos where sexual orientation was indicated. The preliminary research was published in the Journal of Personality and Social Psychology, authored by Michal Kosinski, a professor at Stanford University Graduate School of Business. by Ke Li of UC Berkeley. A deep dive into optimization algorithms for machine learning. Learning to Optimize with Reinforcement Learning There is a paradox in the current [machine learning] paradigm: the algorithms that power machine learning are still designed manually. This raises a natural question: can we learn these algorithms instead? This could open up exciting possibilities: we could find new algorithms that perform better than manually designed algorithms, which could in turn improve learning capability. by Briot of Sorbonne. Deep Learning Techniques for Music Generation — A Survey et al This book is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. by Hezaveh of Stanford. Application of deep learning in analyzing space imagery. Fast automated analysis of strong gravitational lenses with convolutional neural networks et al Physicists at Stanford University have developed a new technique of using neural networks for analyzing gravitational lenses in distant space ( Motherboard ). Resources, Tutorials & Data by Google. Fun, quick way to get introduced to machine learning. Teachable Machine This experiment lets anyone explore how machine learning works, in a fun, hands-on way. You can teach a machine to using your camera, live in the browser — no coding required. You train a neural network locally on your device, without sending any images to a server. That’s how it responds so quickly to you. by Matt Dancho. Tutorial & example of applying machine learning to solve a business problem. HR Analytics: Using Machine Learning to Predict Employee Turnover With advancements in machine learning (ML), we can now get both better predictive performance and better explanations of what critical features are linked to employee attrition. In this post, we’ll use two cutting edge techniques. by Andrew Beam of Harvard. Curated list of publicly available medical data for machine learning. Medical Data for Machine Learning by Chuck-Hou Yee of Insight. Explanation of how to perform automatic segmentation of the right ventricle in cardiac MRI imagery via deep learning. Heart Disease Diagnosis with Deep Learning by Taha Emara. Tutorial to do video eye monitoring via deep learning, which can be used to detect driver drowsiness. Realtime Driver Drowsiness Detection (Sleep Detection) by Peter Roelants of Onfido. Higher-Level APIs in TensorFlow How to use Estimator, Experiment and Dataset to train models. by Aman Agarwal. Comprehensive breakdown & simplified explanation of how AlphaGo works. Explained Simply: How DeepMind taught AI to play video games By . Isaac is an investor at Venrock ( ). If you’re interested in deep learning or there are resources I should share in a future newsletter, I’d love to hear from you. Isaac Madan email is a newsletter of entrepreneurial ideas & perspectives by investors, operators, and influencers. Requests for Startups ** **❤” Please tap or click “︎ to help to promote this piece to others.
