Intel is coming to your University with a new workshop on Machine and Deep Learning. You're invited to join the event to learn about Machine Learning & Deep Learning Fundamentals, train with a step-by-step live development workshop and more. During this afternoon, you will develop your AI skills, learn about exciting new AI projects and meet other like-minded AI enthusiasts & Intel experts. Register now to save your seat!

I am looking for a series of videos that I can use to learn Pytorch. I found out that there are some presentations, but any recommended videos to go through Deep Learning tutorials with Pytorch would be appreciated.

We would love to learn Machine Learning and welcome anyone who wants to learn the same with us!! It is not an instructor-led session - instead a self teaching session where we all can discuss and teach each other. We will be arranging for some basic machine learning material to use as a guide through the learning process.

I was reading this interesting paper on importance weighted active learning: http://cseweb.ucsd.edu/ I'm trying to implement a basic example of active learning using sklearn. I'm looking at how to implement the "implicit" version. However, I don't understand the meaning of "If learn(S (xt, 1)) and learn(S (xt, 0)) both return an answer". Is there anyone who can shed some light or point me to resources that'll help me implement importance weighted active learning?

Nintendo have shared a couple of instructional videos on their YouTube channel. The videos present and explain the possibilities of the upcoming Variety and Robot Nintendo Labo kits. Nintendo have announced their Labo kits back in January, and now we have some more info about them thanks to a couple of instructional videos. The home-built kits made out of cardboard will interact with the Switch to make some interesting creations. The three videos were shared on Nintendo Labo's YouTube channel, and they detail the contents of the kits as well as what you can use them for.

The first event will feature Yann LeCun, Facebook's director of AI research and a member of the New York University faculty. LeCun will inaugurate the series on Tuesday, February 20, 2018, from 10 to 11 am in Downtown Brooklyn, at NYU Tandon's Pfizer Auditorium, 5 MetroTech Center. Registration is free and open to all. His address, "Obstacles to Deep Learning and AI," will explore a new frontier: predictive models that capture the "common sense" exhibited by humans and animals, who often learn by observation and occasional action. LeCun's pioneering work in the application of neural networks to computer vision and other AI areas led to products and services deployed across most technology companies.

For the first time in India, the All India Council for Technical Education (AICTE) has given up to 20 credits for subjects like artificial intelligence, internet of things, machine learning and robotics. In December 2017, the AICTE had revised the curriculum for the four year undergraduate (BTech) program. AI, robotics and IoT had been made mandatory from academic year 2018-19. In a move which is being clearly welcomed by academicians as well as students, over 80 percent of all the technical and engineering colleges in India have agreed to follow the new curriculum as well as the grading system. Now, this new move by AICTE is being hailed by everyone as the students are hoping to get better placements, and employers are expecting to hire more industry-relevant workforce.

This idea is really interesting. Sadly I don't have nearly enough linear algebra experience to understand the details though. Would IRL still be feasible if the state was not explicit? It seems like this technique depends on prior knowledge of the state machine, but from what I understand about deep reinforcement learning, the state may be very complex, and the value function could actually be a deep neural network.

The LU decomposition is found using an iterative numerical process and can fail for those matrices that cannot be decomposed or decomposed easily. A variation of this decomposition that is numerically more stable to solve in practice is called the LUP decomposition, or the LU decomposition with partial pivoting. The rows of the parent matrix are re-ordered to simplify the decomposition process and the additional P matrix specifies a way to permute the result or return the result to the original order. There are also other variations of the LU. The LU decomposition is often used to simplify the solving of systems of linear equations, such as finding the coefficients in a linear regression, as well as in calculating the determinant and inverse of a matrix.

Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM – educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. The course is shy but confident: It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff.