"Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online. If you don't have the relevant math background: There is an incredible amount of free material online that can be used to learn the required math knowledge. Gilbert Strang's course on linear algebra is a great introduction to the field. For the other subjects, edX has courses from MIT on both calculus and probability. If you are interested in learning more about machine learning: Andrew Ng's Coursera class is a popular choice as a first class in machine learning. There are other great options available such as Yaser Abu-Mostafa's machine learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners. Knowledge in machine learning isn't really a prerequisite to learning deep learning, but it does help. In addition, learning classical machine learning and not only deep learning is important because it provides a theoretical background and because deep learning isn't always the correct solution. Geoffrey Hinton's Coursera class "Neural Networks for Machine Learn... covers a lot of different topics, and so does Hugo Larochelle's "Neural Networks Class".

This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochastic gradient descent algorithm we study the impact of synchronization protocol, stale gradient updates, minibatch size, learning rates, and number of learners on runtime performance and model accuracy. We introduce a new learning rate modulation strategy to counter the effect of stale gradients and propose a new synchronization protocol that can effectively bound the staleness in gradients, improve runtime performance and achieve good model accuracy. Our empirical investigation reveals a principled approach for distributed training of neural networks: the mini-batch size per learner should be reduced as more learners are added to the system to preserve the model accuracy. We validate this approach using commonly-used image classification benchmarks: CIFAR10 and ImageNet.

Thank you all for the huge response to this emerging course! We are delighted to have over 2300 students in over 102 different countries and for the overwhelmingly positive and thoughtful reviews. It's such a privilege to share this important topic with everyday people in a clear and understandable way. In this introductory course, the "Backyard Data Scientist" will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "techno sphere around us", why it's important now, and how it will dramatically change our world today and for days to come.

The things that the world's highest achievers spent their entire lives discovering, that no professor or teacher will ever tell you. Because when I was in college, I was mad. I'd just read a book and everything inside was the opposite of what I was learning in all my classes. So I ran into the dean's office and said "I'm literally learning more from the books I get on Amazon for five bucks than these classes that cost thousands of dollars each!" And all she had to tell me is...they're working on it! So when I walked out that day, I swore I'd teach myself the things I should have learned in school.

Associated Press A file picture of the Ares, a humanoid bipedal robot designed by Chinese college students. From I, Robot to Ex Machina to Morgan, the idea of creating robots that can understand, compute and respond to human emotions has been explored in movies for decades. However, a common misconception is that the challenge of creating emotionally intelligent computing systems is too great to be met any time soon. In reality, computers are already demonstrating they can augment -- or even replace -- human emotional intelligence (EQ). Perhaps, surprisingly, it is the lack of emotion in computing systems that places them in a such a good position to be emotionally intelligent -- unlike humans, who aren't always particularly good at reading others, and are prone to missing emotional signals or being fooled by lies.

Etech Global Services is hosting an interactive workshop which will explore the importance of Artificial Intelligence (AI) and Quality Analytics in the contact center this January 17, 2017 at Call Center Week Winter Conference & Expo in New Orleans, Louisiana. Etech's President, Matt Rocco, and Executive Vice President of Customer Experience, Jim Iyoob, will lead the workshop and drive discussion through various interactive activities and in depth Q&A sessions. Artificial intelligence and machine learning are becoming part of the economy in ways Etech could only imagine a decade ago. From self-driving cars to robots, the rapid growth of AI creates countless opportunities to increase productivity and economic growth. Artificial Intelligence is not new, but the underlying technologies have reached an all time high.

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. You learned about backpropagation (and because of that, this course contains basically NO MATH), but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

Machine learning is increasingly becoming mainstream. It promises higher accuracy and better ROI, and has started to emerge as one of the more reliable analytical practices in recent times. It provides organizations with an edge over their competition. That said, building the right team is a tricky challenge. The traditional approach of building an in-house team for same is not only cumbersome, but also takes lot of time to scale.

When Robert Schapire started studying theoretical machine learning in graduate school three decades ago, the field was so obscure that what is today a major international conference was just a tiny workshop, so small that even graduate students were routinely excluded. But it has become one of the hottest fields in computer science, turning once-obscure academic gatherings like the upcoming Annual Conference on Neural Information Processing Systems in Barcelona, Spain, into a sold-out affair attended by thousands of computer scientists from top corporations and academic institutions. "It's been really something to see this field develop, and to see things that seemed impossible become possible in my lifetime," said Schapire, a principal researcher in Microsoft's New York City research lab whose machine learning research is widely used in the field. The NIPS conference, which starts Monday, is so popular because machine learning has quickly become an indispensable tool for developing technology that consumers and businesses want, need and love. Machine learning is the basis for technology that can translate speech in real time, help doctors read radiology scans and even recognize emotions on people's faces.

When students enrolled in USC's daunting neuroscience graduate program needed help cracking a tough project, they all went to Bosco Tjan. It didn't hurt that his advice often came with a free cappuccino. Mara Mather, a professor of gerontology and psychology at USC, described Tjan as an affable, caring presence on campus. He always found time to aid students and professors despite a breathless schedule. In many ways, she said, Tjan was the center's heartbeat.