DL4J is a pretty awesome open source project that works with Spark and Hadoop. Deep Learning 4J also works as a YARN app! It includes Text, NLP, Canova Vectorization Lib for ML, Scientific computing for the JVM, distributed with clusters, and works with CUDA GPU kernels. DL4J is used for anomaly detection (fraud detection), recommender systems, predictive analytics with logs and image recognition. In a related open source project, Skymind built a numerical computing library ND4J, or n-dimensional arrays for Java, essentially porting Numpy to the JVM.
An interesting cheat sheet (a nice infographic!) was published by Microsoft sometime back to help beginning data scientists on how to choose a Machine Learning algorithm for different predictive analytics needs: Classification (to predict categories), Clustering (to discover structure), Regression (to predict values) and Anomaly Detection (to find unusual data points). Here's what Brandon, the author of the article "How to choose algorithms for Microsoft Azure Machine Learning", says about it: "It depends on the size, quality, and nature of the data. It depends what you want to do with the answer. It depends on how the math of the algorithm was translated into instructions for the computer you are using. And it depends on how much time you have.
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.
"Recently acquired by Hewlett Packard Enterprise, SGI is a trusted leader in technical computing with a focus on helping customers solve their most demanding business and technology challenges." Dr. Eng Lim Goh joined SGI in 1989, becoming a chief engineer in 1998 and then chief technology officer in 2000. He oversees technical computing programs with the goal to develop the next generation computer architecture for the new many-core era. His current research interest is in the progression from data intensive computing to analytics, machine learning, artificial specific to general intelligence and autonomous systems. Since joining SGI, he has continued his studies in human perception for user interfaces and virtual and augmented reality.