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.
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.
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.
The acquisition will bring Zementis' predictive analytics to Software AG's real-time streaming analytics platform. Software AG has acquired California-based Zementis for an undisclosed sum in a move designed to bolster its internet of things capability. Zementis offers software for'deep learning' which plays a crucial role in the development of machine learning, data science and fundamental technology that drives artificial intelligence (AI) development. According to Software AG, the advances in machine learning and AI are being applied in the next generation Internet of Things (IoT) such as self-driving cars, personal digital assistants, medical diagnosis, predictive maintenance and robotics. Software AG has already employed Adaptive Decision and Predictive Analytics (ADAPA) from Zementis into its Digital Business Platform to offer its clients with comprehensive insights for real time business analytics.