Deep learning is a branch of machine learning that uses multiple nonlinear processing layers to learn useful representations of features directly from data. Deep learning models can achieve state-of-the-art accuracy in object classification, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. The accuracy of a deep leaning model largely depends on the amount of data used to train the model. The most accurate models may require thousands or even millions of samples, which can take a very long time to train.

Technology that can mimic and improve on the cognitive abilities of human brain has been the stuff of dystopian movie storylines for decades. But for large companies and research labs, such artificial intelligence has been a longstanding pursuit for both day-to-day and groundbreaking uses. Now, a specific breakthrough in AI -- deep learning -- is allowing business to use the vast amounts of newly available data to teach computers how to learn. Deep learning uses layers of algorithms known as neural networks, which are designed to loosely represent the layers of the human brain. These algorithms allow machines to learn patterns.

Fairfax, VA, August 25 2016 – Invincea, the leader in machine learning for endpoint protection, announced today that its deep learning model for analyzing unknown malware is now fully integrated in the VirusTotal site. VirusTotal is a "service that analyzes suspicious files and URLs and facilitates the quick detection of viruses, worms, Trojans, and all kinds of malware"[1]. By integrating with VirusTotal, Invincea is pushing machine learning into mainstream cyber security solutions. By participating in the VirusTotal community, Invincea is continuing to fulfill their three key principles to security market transparency and accountability: participate in independent 3rd party testing, work towards commonly accepted standards, and avoid being a black box. As part of these principles, Invincea became one of the first next gen endpoint security companies to join the Anti-Malware Testing Standards Organization (AMTSO) in June.

The world depends on coral reefs, but they're disappearing – ravaged by climate change, coastal development, overfishing and pollution. With a quarter of Earth's reefs already gone, scientists are racing to save them, and they're getting a big boost from GPU-accelerated deep learning. Although reefs cover less than one percent of the ocean floor, they provide food and shelter for more than a quarter of all marine species, support fish stocks that feed more than a billion people and provide jobs to millions of people in coastal areas. Scientists study images of coral reefs to measure reef health and changes over time. That's now done by human experts, but it's costly and time-consuming.

Along the way, I'll introduce Deep Learning, and add context and background on why the classifier works so well. Here are links to learn more, thanks for watching, and have fun! You can follow me on Twitter at https://twitter.com/random_forests for updates on episodes, and of course - Google Developers.

In this video H2O.ai Deep Learning Engineer and Architect Fabrizio Milo provides on overview of TensorFlow and explains how to the library within the H2O platform. TensorFlow is an open source, deep learning framework utilized by Google and DeepMind.

NVidia's Titan X graphics card, featuring the company's Pascal-powered graphics processing unit driven by 3,584 CUDA cores running at 1.5GHz. As researchers continue to push the boundaries of neural networks and deep learning--particularly in speech recognition and natural language processing, image and pattern recognition, text and data analytics, and other complex areas--they are constantly on the lookout for new and better ways to extend and expand computing capabilities. For decades, the gold standard has been high-performance computing (HPC) clusters, which toss huge amounts of processing power at problems--albeit at a prohibitively high cost. This approach has helped fuel advances across a wide swath of fields, including weather forecasting, financial services, and energy exploration. However, in 2012, a new method emerged.

We'll look at some of the most important papers that have been published over the last 5 years and discuss why they're so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network architecture, while the second half is just a collection of interesting papers in other subareas. The one that started it all (Though some may say that Yann LeCun's paper in 1998 was the real pioneering publication). This paper, titled "ImageNet Classification with Deep Convolutional Networks", has been cited a total of 6,184 times and is widely regarded as one of the most influential publications in the field. Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton created a "large, deep convolutional neural network" that was used to win the 2012 ILSVRC (ImageNet Large-Scale Visual Recognition Challenge). For those that aren't familiar, this competition can be thought of as the annual Olympics of computer vision, where teams from across the world compete to see who has the best computer vision model for tasks such as classification, localization, detection, and more. The next best entry achieved an error of 26.2%, which was an astounding improvement that pretty much shocked the computer vision community. Safe to say, CNNs became household names in the competition from then on out. In the paper, the group discussed the architecture of the network (which was called AlexNet).

Are we ready for Ultron type intelligence? This is part of our special series on Artificial Intelligence (AI). If you are catching it for the first time I'd recommend that you start here for some instrumental background and here where I start building the bigger idea behind AI. In my high school years, I remember a brilliant classmate, Matthew (not quite his real name), who got the necessary points at Advanced Level to study law at a local university. I was proud to see him not long ago appearing in newspapers as a commanding Intellectual Property (IP) lawyer.

In this paper, we address the problem of conditional modality learning, whereby one is interested in generating one modality given the other. While it is straightforward to learn a joint distribution over multiple modalities using a deep multimodal architecture, we observe that such models aren't very effective at conditional generation. Hence, we address the problem by learning conditional distributions between the modalities. We use variational methods for maximizing the corresponding conditional log-likelihood. The resultant deep model, which we refer to as conditional multimodal autoencoder (CMMA), forces the latent representation obtained from a single modality alone to be `close' to the joint representation obtained from multiple modalities. We use the proposed model to generate faces from attributes. We show that the faces generated from attributes using the proposed model, are qualitatively and quantitatively more representative of the attributes from which they were generated, than those obtained by other deep generative models. We also propose a secondary task, whereby the existing faces are modified by modifying the corresponding attributes. We observe that the modifications in face introduced by the proposed model are representative of the corresponding modifications in attributes.