Deep learning, the bridge between artificial intelligence and big data framework
The demands of investment funds and banks on high frequency trading and streaming analytics have pushed them to develop their own frameworks similar to Sparks (SecDb at GS, Athena at JP etc.) These framework originally designed for intensive financial models can now be used for much broader ‘data science’ and ‘machine learning’ problems whether in the trading/compliance/risk areas etc. My presentation will cover technical aspects of my previous experience in teaching undergrad student neural networks at University (MATLAB), My work experience in distributed computing and how the current theory and infrastructure have triggered a recent boom in Machine Learning.
Graduate from Ecole des Mines de Paris In Computer Science and Mathematics, Pierre-Yves has been enthusiastic about applied mathematics and machine learning ever since the start of his career over 10 years ago. As a JAVA and C# developer in different financial institutions, he had to the opportunity to work on multiple projects mostly around High Performance Computing, pricing and latency-sensitive applications. Today, he’s working at Commerzbank on a framework to stream real-time analytics