This documentation is for scikit-learn version 0.17.1 -- Other versions If you use the software, please consider citing scikit-learn. Applications to real world problems with some medium sized datasets or interactive user interface. Examples illustrating the calibration of predicted probabilities of classifiers. Examples concerning model selection, mostly contained in the sklearn.grid_search

Artificial intelligence technologies such as machine learning will play a key role in shaping the future, Google CEO Sundar Pichai said in the company's annual Founders' Letter to stockholders on Thursday. "It's what has allowed us to build products that get better over time, making them increasingly useful and helpful," wrote Pichai, who cited examples such as voice search, translation tools, image recognition and spam filters. The recent victory of DeepMind's AlphaGo software over legendary master Lee Sedol at the ancient game of Go is "game-changing," Pichai added. Far from portending humanity's downfall, however, the victory is ultimately one for the human race, he said. "This is another important step toward creating artificial intelligence that can help us in everything from accomplishing our daily tasks and travels to eventually tackling even bigger challenges like climate change and cancer diagnosis," Pichai said.

Users don't know what they want until you show them. If you build an perfect engine to show perfect recommend items, the success is all yours .This is the main motto for recommendation engine in E-commerce sites,social networks but how to build an perfect engine to recommend perfect recommend items for users. Here is an basic collaborative recommendation engine implementation in python. Have fun for this weekend.

Using stock market prediction algorithm to forecast energy stocks: This Energy Stocks forecast is designed for investors and analysts who need predictions of the best-performing stocks for the whole Energy Industry (See Industry Package). Package Name: Energy Stocks Forecast Length: 30 Days (03/29/16 – 04/29/16) I Know First Average: 36.82% Cliffs Natural Resources Inc.(CLF) grew by 88.89% in just 1-month, was the top performing stock in the Energy Stocks forecast for that time period. Another top performing stock was DNR that grew by 71.56%, with an astonishing return of ten out of the ten stocks that increased in accordance with the algorithm's prediction. CDE and VALE also offered strong returns of 48.90% and 37.29%, Within the predicted 30-days it performed very well in the Energy Package.

By learning everything there is to know about you and your online habits, social network ETER9 promises a kind of digital immortality wherein an artificially intelligent agent continues to post on your behalf long after you're dead. The future is creepier than we ever imagined. ETER9, a startup launched by Portuguese developer Henrique Jorge, is still in the beta phase, but 5,000 people have already signed up for the service. It currently features a Facebook-like newsfeed, and a "cortex" that works much like a Facebook wall. But that's where the similarities end.

Today OpenAI, a non-profit artificial intelligence research company, launched OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Go. OpenAI researcher John Schulman shared some details about his organization, and how OpenAI Gym will make it easier for AI researchers to design, iterate and improve their next generation applications. John studied physics at Caltech, and went to UC Berkeley for graduate school. There, after a brief stint in neuroscience, he studied machine learning and robotics under Pieter Abbeel, eventually honing in on reinforcement learning as his primary topic of interest.

Machine learning and the Internet of Things (IoT) have the power to create an increasingly autonomous grid that can eventually handle billions of endpoints on utility networks. But is the industry truly maximizing the benefits of either technology? Find out how utilities are using IoT and machine learning today, what they're planning for the future, recommendations on improving utilities' adoption of these technologies on a larger scale and more.

About a month into Japan's professional baseball season, the Fukuoka SoftBank Hawks, the 2014 and 2015 national champions, are doing no worse. Masayoshi Son, chairman and CEO of the SoftBank Group, celebrates with fans after the Hawks advanced to last year's national championship series. Masayoshi Son, chairman and CEO of the SoftBank Group, celebrates with fans after the Hawks advanced to last year's national championship series. Masayoshi Son, chairman and CEO of the SoftBank Group, wants to make the Hawks the best baseball team in the world.

About a month into Japan's professional baseball season, the Fukuoka SoftBank Hawks, the 2014 and 2015 national champions, are doing no worse. Many say the team's strong lineup is underpinned by the cash-rich SoftBank Group, a big telecom and technology group. The reality is quite the reverse. Unlike their rivals, the Hawks are a stand-alone club, though one with the financial leeway to allocate profits to areas where the front office sees fit, such as player development and information technology. With the help of its tech-savvy parent, the club may be about to rewrite "Moneyball," the 2003 bestseller about how a Major League Baseball team in the U.S. used statistical analysis to beat high-spending opponents.

Movidius, machine intelligence partner to DJI, FLIR, Google and others, is introducing the first ever powerful deep learning processing accelerator that fits into a tiny USB Stick. It connects to existing systems and increases the performance of neural networking tasks by 20-30X. It performs at over 150GFLOPS while consuming under 1.2W. Called the Fathom Neural Compute Stick, It's basically the world's first supercomputer on a USB device. Developers, researchers, hobbyists (think raspberry pie) and anyone developing deep learning applications will benefit from Fathom.