09-15, 10:30–11:00 (Europe/London), Assembly Room
This talk will demonstrate how deep learning can be used to identify a deep learning framework such as TensorFlow or PyTorch that would best help a developer build out deep neural networks based on how they write and the problems they solve.
Deep Learning Frameworks allow for the easy construction of neural networks and have made Python the go-to language for Artificial Intelligence development. However, the most popular one, TensorFlow, is still in the midst of putting out just its 2nd major release, and many others are equally as new. Because of the intense pace of innovation in the field deep learning, these frameworks are changing as rapidly as networks they are helping to build. If a developer chooses to use a deep learning framework because of a particular feature that it has, it could quickly change drastically or become unavailable all-together. This session will cover the data collected and methods used to align developers with a deep learning framework that is most suitable to their code and allow audience members to receive recommendations via samples of their own Jupyter Notebooks.
Before becoming an AI Advocate at IBM, Nick studied computer science at Purdue University and the University of Southern California, and was a high performance computing consultant for Hewlett-Packard in Grenoble, France. He now specializes in machine learning and utilizes it to understand machine learning developers of various communities, startups, and enterprises in order to help them succeed on IBM's data science platform. He has a strong interest in data science education and open source software.