The final goal is to solve the network using a solver – to cyclically update network parameters optimizing predefined error function.

The era of the one-size-fits-all supercomputer is over. Hewlett-Packard Enterprise (HPE), the market leader in this space, is now producing high performance computing systems for specific needs. The shift is being driven, in part, by the increasing desire for systems that can process data efficiently. HPE on Monday announced a series of new systems targeted at specific processes such as "deep learning." This is a branch of machine learning used, in particular, to analyze images and sound.

With machine learning taking off among hyperscalers and others who have massive amounts of data to chew on to better serve their customers and traditional simulation and modeling applications scaling better across multiple GPUs, all server makers are in an arm's race to see how many GPUs they can cram into their servers to make bigger chunks of compute available to applications. As the GPU Technical Conference hosted by Nvidia is kicking off in San Jose, Hewlett-Packard Enterprise, which is the dominant peddler of servers in the world with Dell nipping at its heels and a slew of others who aspire to be number three, rolled out a new dense hybrid system that can pack twice as many GPU accelerators in a chassis as its predecessor as well as some companion Lustre appliances that will also be able to run object storage from a number of vendors as well. The Apollo 6500 hybrid servers are the follow-ons to the ProLiant SL6000 "scalable systems" product line that originally debuted back in June 2009 to compete against Dell's custom machines that are sold by its Data Center Solutions (DCS) division. The SL6500s, which were dense machines designed explicitly to have lots of GPU accelerators hanging off Xeon CPUs, rolled out shortly after that and were updated last in November 2012. With the SL270s Gen8 node that HPE offered at the time, its densest compute element, a 4U SL6500 enclosure could have two half-width server sleds, each with two Xeon E5 processors and up to eight single-wide Tesla M2070Q, M2075, M2090, or K10 GPU coprocessor cards rated at no more than 225 watts each.

The wave equation was something like Asin(a1) Bsin(a2) C, with a1 and a2 both having different initial phases and frequencies. The LSTM alone couldn't figure out the wave (normalized), but when I fed in x, x' and x'' (first and second derivatives of the value), the model could understandably learn the pattern faster. Now, I have been trying a new technique where I have a'front' LSTM. Now at every iteration, this LSTM's total state (h:c) go through another'meta' LSTM (whose state is 2*state of the front LSTM), and then gets fed back into the front LSTM. The intuition for this, is that the meta-LSTM will provide a higher degree of abstraction/understanding over the trends, and front-LSTM will use the understanding of these predicted trends, to compute the future value.

DeepMind's deep learning software AlphaGo conquered one of the world's greatest Go players, Lee Sedol, last week – a task that many thought would take another decade to accomplish. The AI approaches AlphaGo used to beat Sedol might be indicative of future trends in AI in general – particularly in the domain of healthcare. In the world of one-to-one boardgames Go is special because, despite it's apparent simplicity, the strategies required to play are considered to be intimately "human" in their reliance on intuition above calculation. While the combinations of many chess scenarios can sometimes be searched thoroughly to 3 or 4 moves head, the number of possible Go moves is borderline incalculable, so the same "brute force" computing strategies that allowed IMB's Deep Blue to defeat Gary Kasparov are not possible on a Go board. Where a human Go player can – in theory – play against herself, it's most practical for her to play against another player to practice, train, and come in contact with different game scenarios.

Terms were not disclosed, but it has all the markings of an "acqhire" sort of deal. Founder and CTO Richard Socher announced the deal in a post on the MetaMind website. Socher says on his personal website that his new title is Chief Scientist at Salesforce. MetaMind's area of expertise is deep learning, the subset of artificial intelligence focused on data processing that is en vogue with Google, Facebook and other tech companies. The startup's specialty is natural language processing -- allowing computers to analyze relationships between words.

The program name is Planet and uses earning neural network program. Thanks to Google, a new artificial intelligence system is outperforming humans in spotting the origins of images. Google has unveiled a new system to identify where photos are taken. The task, simple when images contain famous landmarks or unique architecture, goes beyond the overt to examine small clues hidden in the pixels. The program, named PlaNet, uses a deep-learning neural network, which means the more images PlaNet sees, the smarter it gets.

The top 10 deep learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.

The post links to 6 articles covering a number of related topics.