This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems. Since the cheat sheet is designed for beginner data scientists and analysts, we will make some simplified assumptions when talking about the algorithms. The algorithms recommended here result from compiled feedback and tips from several data scientists and machine learning experts and developers.

The company has just launched a new tool called AutoDraw, which you can think of as Microsoft Paint but with a hulking synthetic brain beneath its plain-looking interface. The software is in fact based on the technology that underlies Google's previous doodling experiment, Quick, Draw!. With more data in hand, Google's AI now appears able to work as a recommendation tool rather than a game. And Google certainly isn't alone in trying to crack the problem: at MIT Technology Review's EmTech Digital conference last month, AI firm Gamalon described how it was developing new ways to identify similar scribbling using less training data than traditional AI approaches.

We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax optimization problem. In this paper, we view the problem of training GANs as finding a mixed strategy in a zero-sum game. Building on ideas from online learning we propose a novel training method named Chekhov GAN 1 . On the theory side, we show that our method provably converges to an equilibrium for semi-shallow GAN architectures, i.e. architectures where the discriminator is a one layer network and the generator is arbitrary. On the practical side, we develop an efficient heuristic guided by our theoretical results, which we apply to commonly used deep GAN architectures. On several real world tasks our approach exhibits improved stability and performance compared to standard GAN training.

Here I discuss four interesting mathematical problems (mostly involving famous unsolved conjectures) of considerable interest, and that even high school kids can understand. For the data scientist, it gives an unique opportunity to test various techniques to either disprove or make progress on these problems. The field itself has been a source of constant innovation -- especially to develop distributed architectures, as well as HPC (high performance computing) and quantum computing to try to solve (to non avail so far) these very difficult yet basic problems. And the data sets involved in these problems are incredibly massive and entirely free: it consists of all the integers, and real numbers! The first two problems have been addressed on Data Science Central (DSC) before, the two other ones are presented here on DSC for the first time.

After a 20-year marriage and a separation, I decided to try online dating. I quickly learned to keep the first meeting short, not dinner, in case things didn't go well. On one date I arranged to meet a UCLA law school graduate at a Santa Monica coffee house. As a lawyer myself, I anticipated at least some interesting conversation with a well-educated woman. At first sight, I thought she had sent her mother, not the woman pictured in her dating profile.

"Gaokao" week in China is a stressful time for students taking college entrance exams (like the SAT or ACT in the United States) as students have high hopes of garnering acceptance to a top university and eventually landing a white collar job. With such high stakes some students, however, find themselves resorting to desperate measures for good scores. With cheating being more prevalent during "Gaokao" week, authorities are cracking down on the widespread problem among the teen students who take the two-day exam each year. After years of students using sophisticated technology like small ear pieces and wireless devices designed to look like ordinary belts, pencil erasers and more to cheat, authorities are responding with some counteractive technology. They're using drones, facial recognition software, metal detectors and more to try to eliminate cheating in all forms, reports Reuters. Some provinces in the country require that students go through fingerprint scans as well as facial recognition scans just to get into the exam.

Must have experience managing end-to-end machine learning pipeline from data exploration, feature engineering, model building, performance evaluation, and online testing with TB to Petabyte-size datasets."

This week MapR announced a new solution called Quick Start Solution (QSS), focusing on deep learning applications. MapR touts QSS as a distributed deep learning (DL) product and services offering that enables the training of complex deep learning algorithms at scale. Ted Dunning, MapR chief application architect, explains: "The best approach for pursuing AI/Deep learning is to deploy a scalable converged data platform that supports the latest deep learning technologies with an underlying enterprise data fabric with virtually limitless scale." But being able to run ML or DL on Hadoop does not really make a Hadoop vendor an AI vendor too.

These are heady times in machine learning and artificial intelligence; new algorithms, TensorFlow, and clusters of powerful GPU's are combining to produce powerful systems that can do things like beat the world's best Go player. But with great power comes great responsibility. Let me tell you a story about the unintended consequences of well-meaning machine learning research. I had been working on Amazon.com's You know, the recommender systems that sell you stuff you never knew existed based on your past interests and purchases, and generate a sizable percentage of Amazon's revenue.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)