Trained recurrent networks are powerful tools for modeling dynamic neural computations. We present a target-based method for modifying the full connectivity matrix of a recurrent network to train it to perform tasks involving temporally complex input/output transformations. The method introduces a second network during training to provide suitable "target" dynamics useful for performing the task. Because it exploits the full recurrent connectivity, the method produces networks that perform tasks with fewer neurons and greater noise robustness than traditional least-squares (FORCE) approaches. In addition, we show how introducing additional input signals into the target-generating network, which act as task hints, greatly extends the range of tasks that can be learned and provides control over the complexity and nature of the dynamics of the trained, task-performing network.

Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Reconstruction error from deep autoencoder and statistical process control approach are applied to detect time period with anomalies. Related events are found using time series similarity measures between events and abnormal stat metrics. After training deep autoencoder with DBMS metric data, efficacy of anomaly detection is investigated from other DBMSs containing anomalies. Experiment results show effectiveness of proposed model, especially, batch temporal normalization layer. Proposed model is used for publishing automatic DBMS diagnosis reports in order to determine DBMS configuration and SQL tuning.

This article was written by Adrian Rosebrock. Adrian is an entrepreneur and Ph.D who has launched two successful image search engines, ID My Pill and Chic Engine. If you've been following along with this series of blog posts, then you already know what a hugefan I am of Keras. Keras is a super powerful, easy to use Python library for building neural networks and deep learning networks. In the remainder of this blog post, I'll demonstrate how to build a simple neural network using Python and Keras, and then apply it to the task of image classification. To start this post, we'll quickly review the most common neural network architecture -- feedforward networks.

Artificial Intelligence (AI) and Machine Learning used to be heard when the topic was Big Data Analytics – and maybe in some sci-fi movies- before; but now it is impossible to ignore them with the self-driving cars, knowledge navigators... These terms might be quite widespread but they can lead to confusions as they are very much related and being used interchangeably. Artificial intelligence has a longer history than machine learning. It might sound like a new term but we can say it has been studied and improved over the years since Aristotle introduced syllogism, which was a method of formal and mechanical thought. The real birth of the current understanding however starts in the 1940s and 50s with some scientists from mathematics, engineering, psychology, economics and political science who put the idea of'creating an artificial brain' on the table.

I'm standing in what is soon to be the center of the world, or is perhaps just a very large room on the seventh floor of a gleaming tower in downtown Toronto. Showing me around is Jordan Jacobs, who cofounded this place: the nascent Vector Institute, which opens its doors this fall and which is aiming to become the global epicenter of artificial intelligence. We're in Toronto because Geoffrey Hinton is in Toronto, and Geoffrey Hinton is the father of "deep learning," the technique behind the current excitement about AI. "In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Of the researchers at the top of the field of deep learning, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team. The Vector Institute, this monument to the ascent of Hinton's ideas, is a research center where companies from around the U.S. and Canada--like Google, and Uber, and Nvidia--will sponsor efforts to commercialize AI technologies. Money has poured in faster than Jacobs could ask for it; two of his cofounders surveyed companies in the Toronto area, and the demand for AI experts ended up being 10 times what Canada produces every year. Vector is in a sense ground zero for the now-worldwide attempt to mobilize around deep learning: to cash in on the technique, to teach it, to refine and apply it. Data centers are being built, towers are being filled with startups, a whole generation of students is going into the field.

Amazon, Google, Facebook, and IBM are set to lead the way in Artificial Intelligence. As larger companies, they have the right resources to collect data, and therefore, have more data to work with. Google is most likely in the forefront in terms of deploying machine learning for applications and product development and services. Not only were they the first company to start AI research, but with over 70,000 employees, Google is quite a large company. Moreover, with Google Brain, a deep learning AI research project, Google has an entire team with its own research agenda covering areas that include machine learning, natural language understanding, machine learning algorithms and techniques, and robotics.

Even as machines known as "deep neural networks" have learned to converse, drive cars, beat video games and Go champions, dream, paint pictures and help make scientific discoveries, they have also confounded their human creators, who never expected so-called "deep-learning" algorithms to work so well. No underlying principle has guided the design of these learning systems, other than vague inspiration drawn from the architecture of the brain (and no one really understands how that operates either). Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences. Like a brain, a deep neural network has layers of neurons--artificial ones that are figments of computer memory. When a neuron fires, it sends signals to connected neurons in the layer above.

Since then deep learning has been adopted by industry leaders like Google to recognize objects on android phones, Facebook used deep learning for auto tagging faces, the auto industry used it for self driving cars and there are many other industries which adopted deep learning to solve complex problems. Recently, a deep learning model trained on 150,000 skin cancer images was able to detect skin cancer better than average dermatologist. By understanding and predicting human behavior, machine learning helps create personalized marketing campaigns that increase conversion rates, average transaction size, and frequency of purchase. A trained machine learning model will eventually predict breakdown events long before they happen and save the cost of towing the vehicle.

With the rise of autonomous vehicles, smart video surveillance, facial detection and various people counting applications, fast and accurate object detection systems are rising in demand. These systems involve not only recognizing and classifying every object in an image, but localizing each one by drawing the appropriate bounding box around it. This makes object detection a significantly harder task than its traditional computer vision predecessor, image classification. Fortunately, however, the most successful approaches to object detection are currently extensions of image classification models. A few months ago, Google released a new object detection API for Tensorflow. In my last blog post, I covered the intuition behind the three base network architectures listed above: MobileNets, Inception, and ResNet.

Sentiment analysis is a gateway to AI-based text analysis. For any company or data scientist looking to extract meaning out of an unstructured text corpus, sentiment analysis is one of the first steps which gives a high RoI of additional insights with relatively low investment of time and effort. With an explosion of text data available in digital formats, the need for sentiment analysis and other NLU techniques for analyzing this data is growing rapidly. Sentiment analysis looks relatively simple and works very well today, but we have reached hereafter significant efforts by researchers who have invented different approaches and tried numerous models. In the chart above, we give a snapshot to the reader about the different approaches tried and their corresponding accuracy on the IMDB dataset.