Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I've seen in ML. Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it's widely used in industry, can handle a wide variety of data types, and can be implemented at scale.
UCLA researchers have developed a new laser-based technology to rapidly screen blood samples for the presence of cancer cells. The label-free system measures 16 different physical characteristics of each cell and analyzes the data to identify whether the cell is cancerous. Not having to introduce any labeling chemicals and being gentle on the cells, the technique leaves the cells alive and available for further inspection using other means. It relies on a photonic time stretch microscope and a computer that runs deep learning artificial intelligence algorithms. The microscope can take millions of images per second thanks to unusual optics that produce high quality shots even at this speed.
Deep learning has been very successful in social sciences and specially areas where there is a lot of data. Trading is another field that can be viewed as social science with a lot of data. With the advent of Deep Learning and Big Data technologies for efficient computation, we are finally able to use the same methods in investment management as we would in face recognition or in making chat-bots. In his session at 20th Cloud Expo, Gaurav Chakravorty, co-founder and Head of Strategy Development at qplum, will discuss the transformational impact of Artificial Intelligence and Deep Learning in making trading a scientific process. This focus on learning a hierarchical set of concepts is truly making investing a scientific process, a utility.
Thanks to artificial intelligence, we have autonomous cars, chat bots, and speech recognition. Microsoft's CNTK (Cognitive Toolkit) is one among many platforms that trains computers to learn, and it's getting an upgrade. CNTK drives the Microsoft services Cortana and Skype language translation, and it boasts more than 90 percent accuracy in speech recognition tasks. Microsoft will soon release an upgraded CNTK toolkit, and one hardware maker wants to ensure the toolkit works best on its hardware. Nvidia is partnering with Microsoft to optimize its GPU development tools for CNTK.
An introduction to the field of computer vision and image recognition, and how Deep Learning is fueling the fire of this hot topic. Computer Vision is an interdisciplinary field that focuses on how machines or computers can emulate the way in which humans' brains and eyes work together to visually process the world around them. Research on Computer Vision can be traced back to beginning in the 1960s. The 1970's saw the foundations of computer vision algorithms used today being made; like the shift from basic digital image processing to focusing on the understanding of the 3D structure of scenes, edge extraction and line-labelling. Over the years, computer vision has developed many applications; 3D imaging, facial recognition, autonomous driving, drone technology and medical diagnostics to name a few.