In recent years, deep learning has taken the lead in predictive accuracy in many fields of machine learning, and companies are struggling to keep up with the speed of innovation. Arno Candel demonstrates how successful enterprises can augment simple statistical models with more accurate data-driven models to gain a competitive edge. Arno describes how to build smart applications that include data munging, model training and validation, and real-time production deployment--every step is based on open source code (R, Python, Java, Scala, JavaScript, REST) that runs on distributed platforms including Hadoop, Spark, and standard compute clusters. Arno also presents use cases from verticals including insurance, fraud, churn, fintech, and marketing and offers live demos of smart applications on large real-world datasets in distributed clusters.

The SF Crime challenge: the training set consists of crime-events labeled by a date/time, the police district it occurred in, the lat-lon coordinates, an address, and the crime-category. You're asked to predict the crime-category in a test set. Could one think of the date/time features as "words" generated in a police district (or perhaps yearly/monthly/hourly) "document" with a distribution of crime-category "topics" in the spirit of LDA for document classification? If I'm on the right track, but don't quite have the analog of graphical model elements quite right, could you explain?

If pigeons were trained to detect cancer, maybe cats neurons can be trained to translate Chinese. But you would probably need to connect wires to the cat's brain instead of just working with his vision.

A new low-power, "brain-inspired" supercomputing platform based on IBM chip technology will soon start exploring deep learning for the U.S. nuclear program. Lawrence Livermore National Laboratory announced on Tuesday that it has purchased the platform, based on the TrueNorth neurosynaptic chip IBM introduced in 2014. It will use the technology to evaluate machine-learning and deep-learning applications for the National Nuclear Security Administration. The computer will process data with the equivalent of 16 million neurons and 4 billion synapses and consume roughly as much energy as a tablet PC. Also included will be an accompanying ecosystem consisting of a simulator; a programming language; an integrated programming environment; a library of algorithms and applications; firmware; tools for composing neural networks for deep learning; a teaching curriculum; and cloud enablement.

The radio spectrum is a mess: it's congested, expensive and there's no room for expansion. But DARPA has a plan to change that, by building a system where radio waves can work together using artificial intelligence, rather than fighting for space. DARPA launched its latest Grand Challenge last week, and it plans to encourage researchers around the world to develop "smart systems that collaboratively, rather than competitively, adapt in real time to today's fast-changing, congested spectrum environment... to maximize the flow of radio frequency". That sounds exciting, because making radio frequency flow more easily means -- theoretically, at least -- faster data rates, fewer dropped signals, and cheaper connections. How does it plan to do it?

For much of my career I've worked with technologies for handling "structured information" – that is, data that is well-formed, with understood data types and fields, and almost always either generated by a computer of some sort, or coded by people to be easily understood by a computer. This data was, for many years, the most common form of information available to business. Starting about twenty years ago, some new technologies began to emerge which changed things. And today things are very different. Much of the information we create as people – emails, blog posts, web pages, PDF documents, video, audio, and more – is now conveyed or managed by computers.

Stochastic Gradient Descent is an important and widely used algorithm in machine learning. In this post you will discover how to use Stochastic Gradient Descent to learn the coefficients for a simple linear regression model by minimizing the error on a training dataset. Linear Regression Tutorial Using Gradient Descent for Machine Learning Photo by Stig Nygaard, some rights reserved. Here is the raw data. The attribute x is the input variable and y is the output variable that we are trying to predict.

Many articles have been written about the top machine learning algorithms: click here and here for instance. Most of them seem to define top as oldest, and thus most used, ignoring modern, efficient algorithms fit for big data, such as indexation, attribution modeling, collaborative filtering, or recommendation engines used by companies such as Amazon, Google, or Facebook. I received this morning and advertisement for a (self-published) book called Master Machine Learning Algorithms, and I could not resist to post the author's list of top 10 machine learning algorithms:: Some of these techniques such as Naive Bayes (variables are almost never uncorrelated), Linear Discriminant Analysis (clusters are almost never separated by hyperplanes), or Linear Regression (numerous model assumptions - including linearity - are almost always violated in real data) have been so abused that I would hesitate teaching them. This is not a criticism of the book; most textbooks mention pretty much the same algorithms, and in this case, even skipping all graph-related algorithms. Even k Nearest Neighbors have modern, fast implementations not covered in traditional books - we are indeed working on this topic and expect to have an article published shortly about it.

You could soon be sharing the sidewalk with robots in D.C. A test project will be presented to District leaders to allow delivery robots to operate on city sidewalks. WASHINGTON - You could soon be sharing the sidewalk with robots in D.C. A test project will be presented to District leaders to allow delivery robots to operate on city sidewalks. These robots could be delivering your takeout meal or maybe some fresh groceries.

Donald Trump's speeches are nothing like that, according to Brad Hayes, a MIT scientist who programmed a Twitter bot to sound like him. Called DeepDrumpf, it uses an artificial intelligence algorithm based on Trump's language in hundreds of hours of debate transcripts. Hayes told Tech Insider he has learned a lot over the last few weeks about how Trump talks. Here is how he describes Trump's language, which differs dramatically from past presidential candidates. Trump often uses short, imperative sentences, Hayes says.