The popularity of deep learning is due in part to its capabilities of handling large amounts of data and its efficiency when retraining the model has made it a useful tool when working on real-world data. Nowadays, these abilities are especially useful when trying to recognize patterns from inputs like images or videos, which present two dimensions of the big data world—volume and velocity.
Barbara Fusinska offers an overview of Microsoft Cognitive Toolbox, an open source framework offering various modules and algorithms enabling machines to learn like a human brain dedicated to solving deep learning challenges. It offers a set of built-in components and sophisticated algorithms, allows users to customize the machine-learning process, and enables easy integration with the Azure platform.
Barbara covers basic deep learning concepts and explains how to use Cognitive Toolkit when approaching them. By applying reinforcement learning and neural networks algorithms to several machine-learning challenges, Barbara demonstrates the capabilities of the platform. Along the way, she also covers use cases on using core components when building solutions and publishing them to the Azure cloud and previews the technical roadmap for the rest of 2017.
Barbara Fusinska is a machine learning strategic cloud engineering manager at Google with a strong software development background. Previously, she was at a variety of different companies like ABB, Base, Trainline, and Microsoft, where she gained experience in building diverse software systems, ultimately focusing on the data science and machine learning field. Barbara believes in the importance of data and metrics when growing a successful business. In her free time, Barbara enjoys programming activities and collaborating around data architecture. She can be found on Twitter as @BasiaFusinska and blogs at http://barbarafusinska.com.
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