I've worked on Deep Learning for a few years as part of my research and among several of my related pet projects is ConvNetJS - a Javascript library for training Neural Networks. Javascript allows one to nicely visualize what's going on and to play around with the various hyperparameter settings, but I still regularly hear from people who ask for a more thorough treatment of the topic. This article (which I plan to slowly expand out to lengths of a few book chapters) is my humble attempt. It's on web instead of PDF because all books should be, and eventually it will hopefully include animations/demos etc. My personal experience with Neural Networks is that everything became much clearer when I started ignoring full-page, dense derivations of backpropagation equations and just started writing code. Thus, this tutorial will contain very little math (I don't believe it is necessary and it can sometimes even obfuscate simple concepts). Since my background is in Computer Science and Physics, I will instead develop the topic from what I refer to as hackers's perspective. Basically, I will strive to present the algorithms in a way that I wish I had come across when I was starting out. "…everything became much clearer when I started writing code." You might be eager to jump right in and learn about Neural Networks, backpropagation, how they can be applied to datasets in practice, etc. But before we get there, I'd like us to first forget about all that. Let's take a step back and understand what is really going on at the core. Update note: I suspended my work on this guide a while ago and redirected a lot of my energy to teaching CS231n (Convolutional Neural Networks) class at Stanford. The notes are on cs231.github.io These materials are highly related to material here, but more comprehensive and sometimes more polished. In my opinion, the best way to think of Neural Networks is as real-valued circuits, where real values (instead of boolean values {0,1}) "flow" along edges and interact in gates. However, instead of gates such as AND, OR, NOT, etc, we have binary gates such as * (multiply), (add), max or unary gates such as exp, etc.

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A big benefit of using the Weka platform is the large number of supported machine learning algorithms. The more algorithms that you can try on your problem the more you will learn about your problem and likely closer you will get to discovering the one or few algorithms that perform best. In this post you will discover the machine learning algorithms supported by Weka. How to Use Machine Learning Algorithms in Weka Photo by Eugeniy Golovko, some rights reserved. Weka has a lot of machine learning algorithms.

LONDON: Understanding the hierarchical structure of biological networks like human brain -- a network of neurons -- could be useful in creating more complex, intelligent computational brains in the fields of artificial intelligence and robotics, says a study. Like large businesses, many biological networks are hierarchically organised, such as gene, protein, neural, and metabolic networks. This means they have separate units that can each be repeatedly divided into smaller and smaller subunits. Apple to sell solar energy now Apple is now planning to sell excess solar energy produced at its solar farms in Cupertino and Nevada. To understand as to why biological networks evolve to be hierarchical, researchers from the University of Wyoming and the French Institute for Research in Computer Science and Automation (INRIA) simulated the evolution of computational brain models, known as artificial neural networks, both with and without a cost for network connections.

BOSTON, MA--(Marketwired - Jul 5, 2016) - Jibo Inc., creator of the world's first social robot for the home, is pleased to announce the addition of Professor Michael I. Jordan to the company's advisory board. Jordan is renowned in the scientific community as an expert and leading researcher in the fields of artificial intelligence and machine learning. "Jibo is breaking new ground by bringing a human element to the robot experience -- something I believe the world needs and will benefit from embracing," said Michael I. Jordan, advisory board member of Jibo Inc. "My background and research in AI is uniquely suited to help in advancing Jibo's learning capabilities and developing his role and relationships within the home environment." Currently the Pehong Chen distinguished professor in electrical engineering, computer science and statistics at the University of California, Berkeley, Jordan has developed a wide range of novel methods in machine learning, natural language processing and signal processing. Jibo Inc. will apply artificial intelligence and machine learning techniques to the field of social rapport and relationships.

Artificial intelligence is disrupting everything from board games to the betting industry--but how about one of the world's oldest beverages? London-based IntelligentX is the world's first firm to brew beer with the help of artificial intelligence. The startup is using an algorithm called Automated Brewing Intelligence (ABI) to collect customer feedback data through a Facebook Messenger bot in order to improve the recipes of its beer. "Over the last six months we've iterated the algorithm and improving the algorithm and improving the beers," Hew Leith, co-founder of IntelligentX, tells Newsweek. Four beers have been created to date--Golden AI, Amber AI, Pale AI and Black AI--all of which are brewed along the Bermondsey Beer Mile in London.

Kevin Townsend is a Senior Contributor at SecurityWeek. He has been writing about high tech issues since before the birth of Microsoft. For the last 15 years he has specialized in information security; and has had many thousands of articles published in dozens of different magazines – from The Times and the Financial Times to current and long-gone computer magazines.

For a field supposedly starved of talent, data science seems to have been minting a lot of new experts in a hurry. The depth of interest was on display this week in San Francisco, where 1,600 people turned up for a data science summit organised by Turi, a company run by University of Washington machine learning professor Carlos Guestrin. Mr Guestrin argues that all software applications will need inbuilt intelligence within five years, making data scientists -- people trained to analyse large bodies of information -- key workers in this emerging "cognitive" technology economy. Whether or not he is right about the coming ubiquity, there is already a core of critical applications that depend on machine learning, led by recommendation programmes, fraud detection systems, forecasting tools and applications for predicting customer behaviour. The adaptation of what was until recently the preserve of research scientists into production-grade business applications could point to a profound change in corporate competitiveness. The companies showing off their skills in data science and machine learning at the Turi event -- including Uber, Pinterest and Quora -- were all born in the digital era.

For the past year, Tara Hutchinson has been trying to figure out what will happen to a tall building made from thin steel beams when "the big one" hits. To do that, she has erected a six-story tower that rises like a lime-green finger from atop a shrub-covered hill on the outskirts of San Diego, California. Hundreds of strain gauges and accelerometers fill the building, so sensitive they can detect wind gusts pressing against the walls. Now, Hutchinson just needs an earthquake. In most of the world, this would be a problem.

A team of scientists has developed a model that can predict bat species most likely to transmit Ebola and other filoviruses. Findings highlight new potential hosts and geographic hotspots worthy of surveillance. So reports a new paper in the journal PLoS Neglected Tropical Diseases. Filoviruses have devastating effects on people and primates, as evidenced by the 2014 Ebola outbreak in West Africa. For nearly 40 years, preventing spillover events has been hampered by an inability to pinpoint which wildlife species harbor and spread the viruses.