Machine learning is the study of computer algorithms which adapt and improve through experience, and a form of artificial intelligence.

Machine Learning in Communication Market research report displays the market size, share, status, production, cost analysis, and market value with …

The 776-hour course will help students develop machines for gaming, … has come out with a new Artificial Intelligence and Machine Learning course.

Machine learning can be a valuable tool for attorneys--if they know how to use it.

Can you recognize a digitally manipulated video when you see one? It's harder than most people realize. As the technology to produce realistic "deepfakes" becomes more easily available, distinguishing fact from fiction will only get more challenging. A new digital storytelling project from MIT's Center for Advanced Virtuality aims to educate the public about the world of deepfakes with "In Event of Moon Disaster." This provocative website showcases a "complete" deepfake (manipulated audio and video) of U.S. President Richard M. Nixon delivering the real contingency speech written in 1969 for a scenario in which the Apollo 11 crew were unable to return from the moon.

In the last decade machine learning becomes one of the hottest topics in the world, Andrew Ng considers it as the new electricity. In today's world machine learning powers many of the services we use -- recommendation systems like those on Netflix, YouTube, and Spotify; search engines like Google and Baidu; social-media feeds like Facebook and Twitter; voice assistants like Siri and Alexa. Having known that, let's see how machine learning works. In simple terms, machine learning algorithms use statistics to find patterns in massive amounts of data. The data are also known as the dataset, it's could contain images, texts, words, and clicks.

It is a complex process, one that involves data mining, machine learning, and predictive modeling.

… analytics and machine-learning platform developed in-house to search for and … The HRtech startup aims to foster a community of learners through …

This Machine Learning Chip report highlights the key driving factors, constraint, opportunities, challenges in the competitive market.

Deep learning has rapidly become the state-of-the-art approach for music generation. However, training a deep model typically requires a large training set, which is often not available for specific musical styles. In this paper, we present augmentative generation (Aug-Gen), a method of dataset augmentation for any music generation system trained on a resource-constrained domain. The key intuition of this method is that the training data for a generative system can be augmented by examples the system produces during the course of training, provided these examples are of sufficiently high quality and variety. We apply Aug-Gen to Transformer-based chorale generation in the style of J.S. Bach, and show that this allows for longer training and results in better generative output.