Data scientists and machine learning engineers in India make about one-tenth of what their counterparts in the United States do, a leading global survey shows. The median annual salary in India, based on 450 responses, is $11,715 (Rs 7.5 lakhs), a fraction of the comparable annual earnings in the US ($110,000). The median for all respondents from 52 countries, whose data was considered in the calculations, is $55,441. Kaggle, the world's largest global online community of data scientists, statisticians and machine learning engineers, published its The State of Data Science & Machine Learning annual survey earlier this week, deriving insights on 16,000 respondents in a report that polled the data science and machine learning industry. The Google-owned platform currently boasts of over a million members and is known to attract the world's smartest data scientists by holding public and private data science competitions.

The idea of access to artificial intelligence (often referred to as "AI" for short) for all is still at a nascent stage. Nevertheless, its potential is limitless – from refugee assistance, to helping students at risk of not completing school, to shortening the response time for teens who are in a personal crisis. During the recent Dreamforce conference in San Francisco, several nonprofit leaders discussed how the use of artificial intelligence and other technologies, such as machine learning, can dramatically improve lives – including those of many who live in the most underserved communities. Some of those citizens now benefiting from artificial intelligence include college and university students in Texas. College Forward, based in Austin, offers coaching and mentoring programs to help at-risk students continue their success in higher education so that they can eventually embark on a successful career.

Machine Learning (ML) is one of the hot buzzwords these days, but even though EDA deals with big-data types of issues it has not made much progress incorporating ML techniques into EDA tools. Many EDA problems and solutions are statistical in nature, which would suggest a natural fit. So why is it so slow to adopt machine learning technology, while other technology areas such as vision recognition and search have embraced it so easily? "You can smell a machine learning problem," said Jeff Dyck, vice president of technical operation for Solido Design Automation. "We have a ton of data, but which methods can we apply to solve the problems? That is the hard part. You cannot open a text book or take a course and apply those methods to solve all problems. Engineering problems require a different angle."

A Reddit study group for the free online version of the Stanford class "Machine Learning", taught by Andrew Ng. The purpose of this reddit is to help each other understand the course materials, not to share solutions to assignments. Please follow the Stanford Honor Code. I'm a new user to Reddit, how does this site work? I have a question about the (class / videos / quiz / homework), how can I get help?

By lifting the ReLU function into a higher dimensional space, we develop a smooth multi-convex formulation for training feed-forward deep neural networks (DNNs). This allows us to develop a block coordinate descent (BCD) training algorithm consisting of a sequence of numerically well-behaved convex optimizations. Using ideas from proximal point methods in convex analysis, we prove that this BCD algorithm will converge globally to a stationary point with R-linear convergence rate of order one. In experiments with the MNIST database, DNNs trained with this BCD algorithm consistently yielded better test-set error rates than identical DNN architectures trained via all the stochastic gradient descent (SGD) variants in the Caffe toolbox.

The partially observable hidden Markov model is an extension of the hidden Markov Model in which the hidden state is conditioned on an independent Markov chain. This structure is motivated by the presence of discrete metadata, such as an event type, that may partially reveal the hidden state but itself emanates from a separate process. Such a scenario is encountered in keystroke dynamics whereby a user's typing behavior is dependent on the text that is typed. Under the assumption that the user can be in either an active or passive state of typing, the keyboard key names are event types that partially reveal the hidden state due to the presence of relatively longer time intervals between words and sentences than between letters of a word. Using five public datasets, the proposed model is shown to consistently outperform other anomaly detectors, including the standard HMM, in biometric identification and verification tasks and is generally preferred over the HMM in a Monte Carlo goodness of fit test.

I have this portfolio with jupyter notebooks done by me. Several of them need to be reworked or deleted, but most of them are okay. One of them is similar to things which I did while I worked in a bank. As for the first project - this is my attempt to build a site with handwritten digit recognition system with online training. This portfolio really helped me when I was looking for a job.

Across the world, higher education is increasingly being judged through the lens of employability. More and more, politicians are asking universities how they are preparing students for work, and even tying their funding to their graduates' success in the workplace. In the West, this has mainly been a result of the squeeze on the public purse and – in some countries, at least – an accompanying rise in tuition fees. But there is also growing anxiety about the technological revolution's potential to replace large numbers of human workers with computers and robots if humans can't keep one step ahead in the race to acquire skills. So how well are universities meeting the challenge of preparing graduates for the digital age?

Mitchell Cooper likes to build things, and not just chemistry in the water as a senior captain for the Corona del Mar High boys' water polo team. Cooper is the president of the CdM Robotics club. Now that the boys' water polo season is over, he will turn his attention to that after the holidays. Members of the club compete in the For Inspiration and Recognition of Science and Technology (FIRST) robotics competition. "We build 120-pound robots to compete in a game that's announced to us in January of each year," Cooper said.

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Includes: 40.5 hours on-demand video 20 Articles 2 Supplemental Resources Full lifetime access Access on mobile and TV Certificate of Completion Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.