Adaptive learning rate algorithms such as RMSProp are widely used for training deep neural networks. RMSProp offers efficient training since it uses first order gradients to approximate Hessian-based preconditioning. However, since the first order gradients include noise caused by stochastic optimization, the approximation may be inaccurate. In this paper, we propose a novel adaptive learning rate algorithm called SDProp. Its key idea is effective handling of the noise by preconditioning based on covariance matrix. For various neural networks, our approach is more efficient and effective than RMSProp and its variant.

Deep learning is a very exciting subfield of machine learning that is a set of algorithms, inspired by the structure and function of the brain. These algorithms are usually called Artificial Neural Networks (ANN). Deep learning is one of the hottest fields in data science with many case studies with marvelous results in robotics, image recognition and Artificial Intelligence (AI). This undoubtedly sounds very exciting (and it is!), but it is definitely one of the more complex topics in data science to get into. If you have prior machine learning experience, though, you should be getting started with deep learning pretty easily, as you will have already proven that you have understood, practiced and assimilated the necessary mathematics, statistics and machine learning basics.

This repo contains an incremental sequence of notebooks designed to teach deep learning, Apache MXNet (incubating), and the gluon interface. Our goal is to leverage the strengths of Jupyter notebooks to present prose, graphics, equations, and code together in one place. If we're successful, the result will be a resource that could be simultaneously a book, course material, a prop for live tutorials, and a resource for plagiarising (with our blessing) useful code. To our knowledge there's no source out there that teaches either (1) the full breadth of concepts in modern deep learning or (2) interleaves an engaging textbook with runnable code. We'll find out by the end of this venture whether or not that void exists for a good reason.

The main point is to combine mathematical operation together to form a workflow of choice. The graph takes care of evaluating the gradient of all the inputs to ease up setting up the minimizer. I have aimed for the library to be simple and transparent so that it would be easy to understand and modify to fit individual needs. Performance was not the main objective as there are plenty of fast alternatives; the aim was smaller, educational models. Currently, supports most of the useful matrix operations, the Adam stochastic minimizer as well as modules for simplified deployment of dense, convolution and recurrent (vanilla and LSTM) networks.

I'll explain PyTorch's key features and compare it to the current most popular deep learning framework in the world (Tensorflow). We'll then write out a short PyTorch script to get a feel for the syntax. This library is becoming popular pretty fast, such is the nature of this incredible field. That's what keeps me going.

Washington D.C. – In its most basic form, artificial intelligence is an algorithm that is trained to learn via the data that is fed to it. But what happens when that data is full of bias? "In traditional model building, even with good data we can introduce biases by not constructing the right variables or picking up nuances. A model is a representative of the mechanism that generated the data. So if we don't represent that mechanism correctly, then we are not forecasting correctly, but forecasting something else," explains Oliver Schabenberger, chief technology officer and executive vice president of SAS Institute Inc. to Canadian media at the Analytics Experience 2017.

Hello all, my first post here, nice to meet you! I'm working currently as a backend developer, but I have started to read about all this machine learning stuff - it is going crazy all around job markets - and was thinking about switching, as it should not be hard to learn the stuff (I've got math background). I've read that this kind of science use GPUs a lot to speed up the computations. Right now I am about to build a new home PC and I though I could use new GPU not only for games and cracking neighbours wifi password, but also to learn some ML/DL. I have an opportunity to buy AMD rx 580 (8 GB) card with considerable good price (even in the cryptocurrency mining craze) - it seemed to me as a good choice, considering open source drivers (Linux user) and performance.

After DeepMind has released the Video Pixel Networks paper I started working on a tensorflow implementation of it on this github repository . I finished it so feel free to check it out. And I also wrote a medium post about the architecture and my implementation details.

Researchers at the Stevens Institute of Technology in New York, and the New York Institute of Technology have devised what they claim is a highly effective way to guess passwords using a deep learning tool called Generative Adversarial Networks (GANs). Tests of the'PassGAN' technique, as the researchers are calling it, show the method to be an improvement over state-of-the-art, rules-based password guessing tools such as HashCat and John the Ripper, the researchers said in a recently published technical paper. In their experiments the researchers were able to match nearly 47% -- or some 2,774,269 out of 5,919,936 passwords -- from a testing set comprised of real user passwords that were publicly leaked after a 2010 data breach at RockYou. Overall, the evaluations showed PassGAN outperforming John the Ripper by a factor of two, and being at least as competitive with passwords generated using the best rules from HashCat. When the output from PassGAN was combined with HashCat output the researchers could match about 24% more passwords than generated by HashCat alone.