This is a program for a general feedforward neural network and is intended for educational purposes. It is simple and short, making it easy for a reader to quickly get into the details of how a neural network can be implemented. NeuralNet2.ipynb contains the code for the neural network, the rest of the .ipynb This code accompanies a set of tutorials on neural networks, including a walkthrough of the NeuralNet2.ipynb, NeuralNet2.ipynb is a vectorized implementation of a general feedforward neural network in Python Two example applications are provided.

This article is featured in the new DZone Guide to Big Data Processing, Volume III. Get your free copy for more insightful articles, industry statistics, and more. In the past few decades, computer systems have achieved a whole lot. They have managed to organize and catalog the information produced by our civilization as a whole. They have relieved us from stringent cognitive tasks and increased our productivity significantly. One could say that where the industrial revolution automated labor, the digital revolution has automated cognitive labor. This statement isn't entirely correct however, if it was we would all be without a job.

Hewlett Packard Enterprise Co. (HPE) has updated its Vertica analytics suite while introducing new machine learning capabilities to Haven OnDemand. Vertica is software that analyzes structured data, for example information that's stored in rows and fields. But with the new update, Vertica 8, codenamed "Frontloader", HPE has introduced a unified architecture as well as in-database analytics capabilities. According to HPE, Vertica 8 has been designed to help customers extract intelligence from data that resides in multiple silos throughout the data center, be it on-premises, in private and public clouds, or in Hadoop data lakes. Meanwhile, Vertica's new in-database machine learning algorithms enable users to create and deploy R-based machine learning models directly inside the software.

Microsoft is working with Liebherr's appliance division to rebuild the refrigerator and make it smarter, faster, strong; well, maybe just smarter. The new collaboration between the two will see Microsoft provide computer vision technology, via its Microsoft Cognitive Services Computer Vision API, to let the fridge identify objects contained within. Why would you want a fridge that knows what it's holding? It'll save you those extra return trips to the grocery store for things you forgot, for one. The deep learning algorithms in use will be able to learn new food types based on its experience from processing millions of generic food packaging images, and it should be able to get smarter very quickly while in use when and if it eventually comes to market, using data gathered from a wide pool of real-world users. Other fridges have the ability to let you peer inside remotely, but Microsoft's data science team worked directly with Liebherr on this prototype to make a learning fridge that won't force you to rely on your pathetic human eyes looking at a grainy image over a poor cellular connection to roll the dice while shopping.

The Seattle tech titan hired eBay's head of artificial intelligence, Hassan Sawaf, to lead its own A.I. operation in Palo Alto, Calif. Sawaf is now Director of Artificial Intelligence at Amazon-owned A9 Labs, the Wall Street Journal reports. Poaching Sawaf from rival eBay is the latest in a series of moves this week that indicate Amazon is doubling down on A.I. The company just acquired a machine learning team in Cambridge, UK and used some of the 100 million Amazon Alexa fund to invest in natural language processing startup DefinedCrowd. In 2012, Jeff Bezos provided 2 million to fund machine learning professorships for Turi CEO Carlos Guestrin and his wife, Emily Fox at the University of Washington.

The release continues, "The purpose of the research collaboration between UCLH and DeepMind is to develop artificial intelligence technology to assist clinicians in the segmentation process so that it can be done more rapidly but just as accurately. Clinicians will remain responsible for deciding radiotherapy treatment plans but it is hoped that the segmentation process could be reduced from up to four hours to around an hour. The research involves anonymised radiotherapy images of up to 700 former head and neck cancer patients who have consented to their data being used for research purposes. Dr Yen-Ching Chang, clinical lead for radiotherapy at UCLH, said: 'This is very exciting research which could revolutionise the way in which we plan radiotherapy treatment. Developing machine learning which can automatically differentiate between cancerous and healthy tissue on radiotherapy scans will assist clinicians in planning radiotherapy treatment. This has the potential to free up clinicians to spend even more time on patient care, education and research, all of which would be to the benefit of our patients and the populations we serve'."

A typical nightmare scenario goes something like this: Robots first replace autoworkers on the assembly line. Then they move into white-collar jobs, writing articles, drafting legal documents and reading X-rays. Finally, the robots, growing ever smarter through machine learning and Big Data, displace even the most highly trained workers. Another scenario: Robots become so intelligent that they not only can beat people in chess and on Jeopardy!, but they also think faster, better and more analytically than any of us. Milind Tambe thinks these dystopian visions, so popular these days, miss the mark.

Google's DeepMind is aiming to help cut the time spent identifying key areas to treat, and avoid, in radiotherapy. Google's DeepMind is partnering with the UK's NHS to explore how machine learning could help doctors treat head and neck cancers. DeepMind, the Google subsidiary that beat a human contestant in the notoriously complex game of Go and is helping cut Google's datacenter costs, will be conducting cancer treatment research at the Radiotherapy Department at University College London Hospitals (UCLH) NHS Foundation Trust. While privacy and regulation will slow the pace of adoption, AI will bring some profound changes to healthcare. As the AI-research unit notes in a blog post, radiotherapy involving sensitive parts of the body, such as the mouth and sinuses, requires careful planning prior to treatment to avoid damaging key nerves and organs.

When it comes to a movie about the skills modern artificial intelligence can possess, it only seems appropriate to have an actual AI machine create a trailer for said film. Kate Mara's upcoming movie, Morgan, follows a corporate risk management consultant who has to decide whether she should kill an artificial intelligent being. To make the best trailer, IBM's Watson computer was consulted and tasked with making the scariest promotional video possible. The question that IBM's team ran into is how do you teach a machine driven by logic, algorithms and math to incorporate concepts like fear. In order to program Watson to understand what fear is, the research team at IBM got Watson to analyze 100 classic horror movies, examining each scene for consistencies and triggers that lead to the scarier aspects of the films.

Whether you believe the buzz about artificial intelligence is merely hype or that the technology represents the future, something undeniable is happening. Researchers are more easily solving decades-long problems like teaching computers to recognize images and understanding speech at a rapid space, and companies like Google goog and Facebook fb are pouring millions of dollars into their own related projects. For one thing, advances in artificial intelligence could eventually lead to unforeseen consequences. University of California at Berkeley professor Stuart Russell is concerned that powerful computers powered by artificial intelligence, or AI, could unintentionally create problems that humans cannot predict. Consider an AI system that's designed to make the best stock trades but has no moral code to keep it from doing something illegal.