As recently as 2013, the [deep learning] space saw fewer than 10 deals. Computer Vision: Startups here are using deep learning for image recognition, analytics, and classification. Aerial image analytics startup Terraloupe was seed-funded this year by Germany-based Bayern Kapital. New York-based Calrifai -- backed by investors including Google Ventures, Lux Capital, and NVidia -- entered the R/GA accelerator this year, after raising 10M in Series A in Q2'15. Captricity, which extracts information from hand-written data, has raised 49M in equity funding so far from investors including Social Capital, Accomplice, White Mountains Insurance Group, and New York Life Insurance Company.

Organizations are moving more and more of their analytics workloads to the cloud in a bid to reduce operating expenses. One of the vendors trying to capitalize on the trend is Microsoft Corp., which last week rolled out a set of major improvements to its managed Hadoop service in an effort to attract more enterprise customers. Arguably the most important addition is support for the new LLAP (Live Long And Process), a method of caching data in memory to speed up database analytics in the Apache Hive data warehouse. It enables analysts to run database queries up to 25 times faster than before. It's joined by a set of security features that make it possible to regulate who accesses what data, monitor activity and use a company's internal cryptographic keys for encryption.

If you are NOT using Model Evaluation Library you may skip this release. CNTK (http://www.cntk.ai/), the Computational Network Toolkit by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. This project has adopted the Microsoft Open Source Code of Conduct.

Originally published in the ITS Ghaziabad 2nd CXO Meet Souvenir, "Digital India Mission: Transforming India for Tomorrow." Achievements in machine learning are coming at ever-increasing rapidity over the past several months. You are likely familiar with the recent accomplishments associated with machine learning, especially those of so-called deep learning, or the use of multi-layered artificial neural networks. These specific achievements include the high profile AlphaGo and Deep Dream, along with numerous others in the realms of computer vision and natural language processing. Interestingly, a number of these recent mainstream successes are primarily attributable to Google in one form or another.

Artificial intelligence driven by deep learning often runs on many computer chips working together in parallel. But the deep-learning algorithms, called neural networks, can run only so fast in this parallel computing setup because of the limited speed with which data flows between the different chips. The Japan-based multinational Fujitsu has come up with a novel solution that sidesteps this limitation by enabling larger neural networks to exist on a single chip. The neural networks used in deep learning typically run on graphics processing units (GPUs) that originated as components for generating and displaying images. By creating an efficiency shortcut in the calculations performed by neural networks, Fujitsu researchers reduced the amount of internal GPU memory used by 40 percent.

This paper makes a small modification to a standard CNN architecture to improve results with small datasets. This shows a good improvement on CIFAR10/100 when trained with 40% of the data (usually 2-6%). I wish the authors shared their code though. They specifically mention using keras, so it's cant be more than a few hundred lines of python.

If you have been following Data Science / Machine Learning, you just can't miss the buzz around Deep Learning and Neural Networks. Organizations are looking for people with Deep Learning skills wherever they can. From running competitions to open sourcing projects and paying big bonuses, people are trying every possible thing to tap into this limited pool of talent. Self driving engineers are being hunted by the big guns in automobile industry, as the industry stands on the brink of biggest disruption it faced in last few decades! If you are excited by the prospects deep learning has to offer, but have not started your journey yet – I am here to enable it.

After some months reading about deep learning research paper, i feel that there are not many original works like in (bayesian learning, kernel learning). I mean there are many papers just improve something, or combine algorithms, appear a lot even if at Top-venues. What do your think about that? Sorry if i made something wrong.

The Spain-headquartered startup, which today is announcing 6 million in Series A funding, is using deep learning to help startups and larger companies recruit developers. Specifically, its AI tech is analysing the code of millions of developers via their open source contributions in order to match them to appropriate job openings. Meanwhile, the company, which is only in its second year of operation, say it's already close to being profitable and on track to close the year something approaching 1 million in revenue. It also plans to further develop its product, including applying its AI-driven analysis of code to other products aimed at developers, both free and paid-for.

The Spain-headquartered startup, which today is announcing 6 million in Series A funding, is using deep learning to help startups and larger companies recruit developers. Specifically, its AI tech is analysing the code of millions of developers via their open source contributions in order to match them to appropriate job openings. "We use this analysis to understand how good they are at any given language and framework and match them with companies looking for developers," is how Source{d} co-founder and COO Jorge Schnura explains it. He also says it isn't just about identifying code quality or a developer's ability, but also coding style and other nuances that differentiates one developer from another. "We can [find] people who are similar to your team," adds Schnura. "This is all unsupervised learning since we don't tell our algorithms which features to look for, it defines them itself".