Feel free to test any machine learning, deep learning or whatever algorithm you think it could fit, we only ask for acknowledging the source and please let us know of your performance!

Sprinkled throughout Andrew Ng's machine learning class is a lot of practical advice for applying machine learning. That's what I'm trying to compile and summarize here. The key is dividing data into training, cross-validation and test sets. The test set is used only to evaluate performance, not to train parameters or select a model representation. The rationale for this is that training set error is not a good predictor of how well your hypothesis will generalize to new examples.

In the data science course that I instruct, we cover most of the data science pipeline but focus especially on machine learning. Besides teaching model evaluation procedures and metrics, we obviously teach the algorithms themselves, primarily for supervised learning. Near the end of this 11-week course, we spend a few hours reviewing the material that has been covered throughout the course, with the hope that students will start to construct mental connections between all of the different things they have learned. One of the skills that I want students to be able to take away from this course is the ability to intelligently choose between supervised learning algorithms when working a machine learning problem. Although there is some value in the "brute force" approach (try everything and see what works best), there is a lot more value in being able to understand the trade-offs you're making when choosing one algorithm over another.

Virtual reality may be generating most of the buzz today, but another major tech shift looms much closer on the horizon: machine learning. The technology has already made inroads with the public through platforms such as Amazon's Echo and Google's Deep Dream Generator. But its influence will extend beyond voice-controlled speakers and AI-enhanced art, effecting a sea of change for businesses of all sizes. It will be a few years before we witness machine learning's breakthrough moment, but it's coming -- and it will change everything. Humans could be incredibly effective given endless timelines, budget, and energy.

After months of physician-failed diagnosis, a super computer steps in and saves the life of a female patient from Japan, suffering from leukemia. IBM Watson Health has committed to developing a partnership between humanity and technology with the goal of transforming global health. With the ability to read 40 million documents in 15 seconds, IBM's Watson –super computer powered with artificial intelligence- studied the patient's medical records for ten minutes and was able to compare her type of cancer against 20 million oncological records, according to International Business Times. Physicians in Japan decided to try out IBM's Watson on patients after all other treatment options had failed. The Watson revealed that the patient's condition was another form of leukemia and required a different treatment from the one originally prescribed.

A major conference on genomics has moved its West Coast location to San Diego, a leading hub of genomic technology. The Festival of Genomics California runs Tuesday and Wednesday, Sept. 20-21 at the San Diego Convention Center The festival itself is free, People can register at j.mp/fog2016. A pre-event "Base Camp on Monday, Sept. 19 costs 699; information is available at j.mp/2016basecamp. This year's Festival offers sessions on clinical access to genomic medicine, applied technology and data sharing, along with artificial intelligence and machine learning applications. Tuesday is devoted to the effect of genomics on health care, especially in pediatrics.

As industry embraces AI, computers aren't the only ones that have to learn new tricks. Jen-Hsun Huang: 2015 was a big year. Artificial intelligence is moving into the commercial world. AI has been worked on for many years, largely in research. Various aspects of commercial use of AI, otherwise known as machine learning, is used for advertising and web searches and things like that. It wasn't until the last few years that AI could do things that people can't do. Several milestones were achieved in 2015 in particular that made it possible for us to use it in all kinds of areas. Yes, in an area of AI called "deep learning." The system basically learns by itself using a lot of data and computation.

The big data analytics market has seen rapid growth in recent years. Part of this trend includes the increased use of machine learning (Deep Learning) technologies. Indeed, machine learning speed has been drastically increased though the use of GPU accelerators. The issues facing the HPC market are similar to the analytics market -- efficient use of the underlying hardware. A position paper from the third annual Big Data and Extreme Computing conference (2015) illustrates the power of co-design in the analytics market.

While they started out as technological curiosities -- pet projects of computer scientists at MIT, Stanford, and other tech havens -- chatbots are now evolving into the next big interface for doing anything and everything. Ordering flowers, checking the weather, hailing a ride … a few short years ago we'd say, "There's an app for that." But more and more it seems we're able to say, "There's a chatbot for that." One of the main drivers of the rise of the chatbots, of course, is the rise of 1:1 messaging. While AOL and MSN Messenger offered us a glimpse of the future with SmarterChild, messaging platforms like Facebook Messenger and Slack are now helping to elevate chatbots from interesting playthings to powerful tools.

In the deadliest attack against Indian forces in more than a decade, militants sneaked into an army encampment in the disputed territory of Kashmir early Sunday and opened fire on sleeping soldiers, killing at least 17 and wounding dozens. The four assailants, who also threw grenades that caused tents and temporary shelters to catch fire at the army brigade headquarters at Uri, were killed in a gun battle with security forces that lasted six hours, authorities said. Indian officials blamed the Pakistan-based militant group Jaish-e-Mohammed for the attack, saying it had recovered weapons from the assailants that carried Pakistani markings. Lt. Gen. Ranbir Singh, the director of military operations, said he contacted his Pakistani counterpart to convey "serious concerns." Indian Home Minister Rajnath Singh was more pointed, saying on Twitter: "Pakistan is a terrorist state, and it should be identified and isolated as such."