As demand for artificial intelligence grows, so does hunger for the computer power needed to keep AI running. Lightmatter, a startup born at MIT, is betting that AI's voracious hunger will spawn demand for a fundamentally different kind of computer chip--one that uses light to perform key calculations. "Either we invent new kinds of computers to continue," says Lightmatter CEO Nick Harris, "or AI slows down." Conventional computer chips work by using transistors to control the flow of electrons through a semiconductor. By reducing information to a series of 1s and 0s, these chips can perform a wide array of logical operations, and power complex software.

Deep Learning for Trading with Python (Tensorflow and Keras) Learn how to use deep learning to develop robust and profitable trading strategies like the professional quant traders. This course teach you about concepts of deep learning and other machine learning models for Trading. Such techniques are being used by Investment firms and professional traders to make significant return on their trading capital. The deep learning models in this course will be used to develop a powerful swing trading strategy. It is like no other course out there.

This post is an introduction to time-series forecasting with torch. Central topics are data input, and practical usage of RNNs (GRUs/LSTMs). Upcoming posts will build on this, and introduce increasingly involved architectures.

IIT Ropar is an engineering, science, and technology higher education institute located in Rupnagar, Punjab, India, imparting state-of-the-art technical education in a variety of fields. Its emphasis on promoting cutting-edge research and high quality publications is the key to its recognition in the international research community. It is a well reputed institute, having secured high ranks in'Times Higher Education (THE) World University Rankings 2020', 'QS India Rankings 2020', and Union HRD Ministry's'National Institutional Ranking Framework (NIRF)', among others.

The year 2018 and beyond is all set to witness an upward trend for professionals with skills in emerging technologies into Artificial Intelligence (AI), Machine learning and Deep Learning. Professionals with emerging technologies capabilities will continue to be the most sought after by recruiters and business enterprises in 2018 and beyond. With an increasing impetus on Digital India, the analytics and allied industries will be in need of 50 percent more workforce. According to TeamLease Services, a leading recruitment company, Artificial Intelligence alone will create 2.3 million jobs globally by 2020. AI with its inherent capabilities to successfully analyze both structured and unstructured data will help companies offer customized solutions and instructions to employees in real-time.

In this you will learn how to create and use a neural network to classify articles of clothing. To achieve this, we will use a sub module of TensorFlow called keras. Before we dive in and start discussing neural networks, I'd like to give a breif introduction to keras. "Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Keras is a very powerful module that allows us to avoid having to build neural networks from scratch. It also hides a lot of mathematical complexity (that otherwise we would have to implement) inside of helpful packages, modules and methods. In this guide we will use keras to quickly develop neural networks. So, what are these magical things that have been beating chess grandmasters, driving cars, detecting cancer cells and winning video games? A deep neural network is a layered representation of data. The term "deep" refers to the ...

What is it about Python--the language, the ecosystem, the development processes around them--that has made it into such a favorite for data science? Python has long enjoyed growing popularity in many areas of software development--scripting and process automation, web development, general applications. More recently it has become a leading language in machine learning. In this article we'll look at the four major reasons why Python has become a juggernaut in that field. The first major reason is of a piece with why Python has become a general success story: the language makes things simple and keeps them simple.

If you are still having any trouble understanding the process, it may help to think of all our workers as individuals working on the same puzzle problem. At the end of the epoch, they all hand their findings back to the master node, which combines the partial solutions each one has submitted. Then everyone gets a copy of this combined solution, which is still not complete, and they start working on it again for another epoch. The difference is that now they have a head start thanks to everyone's combined work.

In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of the potential for an adverse event in the future. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with carefully designed binary classifier leveraging a Multi-Head Convolutional Neural Network-Recurrent Neural Network (MHCNN-RNN) architecture. Multi-class classifiers are then created and compared, enabling the prediction of different adverse events for any given flight by combining binary classifiers, and by modifying the MHCNN-RNN to handle multiple outputs. Results obtained showed that the multiple binary classifiers perform better and are able to accurately forecast high speed and high path angle events during the approach phase. Multiple binary classifiers are also capable of determining the aircraft's parameters that are correlated to these events. The identified parameters can be considered precursors to the events and may be studied/tracked further to prevent these events in the future.

Alzheimer's disease (AD) is the most common age-related dementia. It remains a challenge to identify the individuals at risk of dementia for precise management. Brain MRI offers a noninvasive biomarker to detect brain aging. Previous evidence shows that the brain structural change detected by diffusion MRI is associated with dementia. Mounting studies has conceptualised the brain as a complex network, which has shown the utility of this approach in characterising various neurological and psychiatric disorders. Therefore, the structural connectivity shows promise in dementia classification. The proposed BrainNetGAN is a generative adversarial network variant to augment the brain structural connectivity matrices for binary dementia classification tasks. Structural connectivity matrices between separated brain regions are constructed using tractography on diffusion MRI data. The BrainNetGAN model is trained to generate fake brain connectivity matrices, which are expected to reflect latent distribution of the real brain network data. Finally, a convolutional neural network classifier is proposed for binary dementia classification. Numerical results show that the binary classification performance in the testing set was improved using the BrainNetGAN augmented dataset. The proposed methodology allows quick synthesis of an arbitrary number of augmented connectivity matrices and can be easily transferred to similar classification tasks.