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.

Machine learning (ML) is all the hotness right now. There is literally a new story every week about a complicated problem that was solved using ML. Most of my coworkers and friends in the industry have expressed interest in learning about ML, but have not been able gain a foot hold. I believe the problem is that the current books and online classes are overwhelming; scaring away students before they have a chance to build up confidence. The problem I had when I started learning ML was that the tutorials insisted on teaching the math that is fundamental for machine learning to work.

Watch on Udacity: https://www.udacity.com/course/viewer... Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262

In this talk, Prof. Iiyoshi goes head to head with an AI questioning the fate of education and lifelong learning! Toru Iiyoshi was previously a senior scholar and Director of the Knowledge Media Laboratory at the Carnegie Foundation for the Advancement of Teaching (1999-2008), and Senior Strategist in the Office of Educational Innovation and Technology at Massachusetts Institute of Technology (2009-2011). He is the co-editor of the Carnegie Foundation book, "Opening Up Education: The Collective Advancement of Education through Open Technology, Open Content, and Open Knowledge" (MIT Press, 2008) and co-author of three books including "The Art of Multimedia: Design and Development of The Multimedia Human Body" and numerous academic and commercial articles. He received the Outstanding Practice Award in Instructional Development and the Robert M. Gagne Award for Research in Instructional Design from the Association for Educational Communications and Technology. Currently, he is the director and a professor of the Center for the Promotion of Excellence in Higher Education (CPEHE) at Kyoto University.

Traditional marketing analytics tools provide perspective into yesterday's performance. But, in order to continuously adapt and improve upon the customer journey in real-time, we need to be able to see into the future. What content will interest our customers? Where will be the best place to engage them? What will be the best time to reach out?

Slate and Future Tense are discussing Mr. Robot and the technological world it portrays throughout the show's second season. You can follow this conversation on Future Tense, and Slate Plus members can also listen to Hacking Mr. Robot, a members-only podcast series featuring Lily Newman and Fred Kaplan. In this episode of Hacking Mr. Robot, Fred and Lily discuss Episode 8: "eps2.6succ3ss0r.p12." They're joined by special guest Matt Devost, a cybersecurity expert and the CEO of FusionX. Fred Kaplan is the author of Dark Territory: The Secret History of Cyber War.

Why guess what will happen in the future? Put your computer to work and allow it to predict it for you. Machine Learning allows computers to learn from and make predictions on data, saving you the headache of trying to do it. With the Complete Machine Learning Bundle you can learn all you need to know about Machine Learning. You'll get over sixty hours of learning about artificial intelligence with courses on quantitative trading, R, Hadoop and MapReduce, Java, decision trees and random forests, deep learning and computer vision, and Python.

The Hidden Markov Model or HMM is all about learning sequences. A lot of the data that would be very useful for us to model is in sequences. Stock prices are sequences of prices. Language is a sequence of words. Credit scoring involves sequences of borrowing and repaying money, and we can use those sequences to predict whether or not you're going to default.

During the E-step we are calculating the expected value of cluster assignments. During the M-step we are calculating a new maximum likelihood for our hypothesis. Bio: Elena Sharova is a data scientist, financial risk analyst and software developer. She holds an MSc in Machine Learning and Data Mining from University of Bristol.

Artificial Intelligence, or AI, is all around us and we might not even realize it! As a technology that imitates human decision making, it is present in a lot of what we use every day. AI can be found in Amazon.com Facebook tells you whom you could know and want to be friends with. Cars can automatically park for you.