Mechanical devices such as engines, vehicles, aircrafts, etc., are typically instrumented with numerous sensors to capture the behavior and health of the machine. However, there are often external factors or variables which are not captured by sensors leading to time-series which are inherently unpredictable. For instance, manual controls and/or unmonitored environmental conditions or load may lead to inherently unpredictable time-series. Detecting anomalies in such scenarios becomes challenging using standard approaches based on mathematical models that rely on stationarity, or prediction models that utilize prediction errors to detect anomalies. We propose a Long Short Term Memory Networks based Encoder-Decoder scheme for Anomaly Detection (EncDec-AD) that learns to reconstruct 'normal' time-series behavior, and thereafter uses reconstruction error to detect anomalies. We experiment with three publicly available quasi predictable time-series datasets: power demand, space shuttle, and ECG, and two real-world engine datasets with both predictive and unpredictable behavior. We show that EncDec-AD is robust and can detect anomalies from predictable, unpredictable, periodic, aperiodic, and quasi-periodic time-series. Further, we show that EncDec-AD is able to detect anomalies from short time-series (length as small as 30) as well as long time-series (length as large as 500).

This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of loss adjusters, social media comments, claim documents and review of medical doctors etc. Unstructured data has massive potential but has never been traditionally considered as a source of insight before. Deep Learning is becoming the method of choice for its exceptional accuracy and capturing capacity for unstructured data.

Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems. Which library should you use and which techniques should you focus on? In this post you will discover a 14-part crash course into deep learning in Python with the easy to use and powerful Keras library. This mini-course is intended for python machine learning practitioners that are already comfortable with scikit-learn on the SciPy ecosystem for machine learning. Applied Deep Learning in Python Mini-Course Photo by darkday, some rights reserved. Before we get started, let's make sure you are in the right place.

This has been one of the most intriguing questions in science fiction and philosophy since the advent of machines. With modern technology, such questions are no longer bound to creative conjecture. Machine learning is all around us. From deciding which movie you might want to watch next on Netflix to predicting stock market trends, machine learning has a profound impact on how data is understood in the modern era. This tutorial aims to give you an accessible introduction on how to use machine learning techniques for your projects and data sets. In just 20 minutes, you will learn how to use Python to apply different machine learning techniques -- from decision trees to deep neural networks -- to a sample data set.

Convolutional Neural Networks (CNN) are biologically-inspired variants of MLPs. From Hubel and Wiesel's early work on the cat's visual cortex [Hubel68], we know the visual cortex contains a complex arrangement of cells. These cells are sensitive to small sub-regions of the visual field, called a receptive field. The sub-regions are tiled to cover the entire visual field. These cells act as local filters over the input space and are well-suited to exploit the strong spatially local correlation present in natural images.

DeepMind is an artificial intelligence lab in London that creates what are known as general purpose self-learning algorithms. The company, acquired by Google in 2014 for a reported 400 million, is best-known for creating software „agents" that have mastered games like…

DeepMind is an artificial intelligence lab in London that creates what are known as general purpose self-learning algorithms. The company, acquired by Google in 2014 for a reported 400 million, is best-known for creating software "agents" that have mastered games like Go and Space Invaders but it also wants to apply its technology to healthcare. Mustafa Suleyman, DeepMind cofounder and head of DeepMind Health, gave a talk at the King's Fund in London this week where he explained how the company is working with the NHS and what kind of benefits patients can expect to see in the long run. The company operates independently of Google and creates software that can think for itself. In order to create this kind of AI software, DeepMind draws on huge data sets that can help to teach DeepMind's AI how to perform certain tasks.

This post was first published on my Linkedin page and posted here as a contributed post. In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results.

An iOS app has gone viral in post-Soviet states this past week, racking up over 650,000 downloads and the top spot in app stores around the region. Russian internet giant Mail.Ru even announced an investment into the product yesterday – a 10 percent stake that reportedly amounts to 2 million. The Russian-made photo app, Prisma, allows users to customize their images by feeding photos through an artificial intelligence that "repaints" them in the stye of great artists like Van Gogh, Munch, and Picasso. Unlike many other photo apps, Prisma doesn't simply slap a filter on top. Instead, the AI completely reinterprets the images using a deep learning method known as convolutional neural networks.

"Our research with DeepMind has the potential to revolutionise the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye diseases such as age-related macular degeneration," said Professor Sir Peng Tee Kaw, the head of Moorfields' ophthalmology research centre. DeepMind, which Google paid 400 million to acquire two years ago, hopes to use artificial intelligence to advance medical and climate research after its software defeated the world champion at the ancient Chinese board game Go.