Researchers have built probabilistic models and deep learning models that have provided benefits in various domains. The probabilistic programming platforms and languages of today empower non-experts to create and apply such probabilistic models based on Bayesian inference techniques. In this tutorial, we combine the power of Edward and TensorFlow to teach how to apply probabilistic programming and deep learning for use cases such as dimensionality reduction and classification in computer vision and image processing.

For more information about the tutorial, please refer to https://ai-vidya.github.io/PP-Tutorial/.

Amita Kapoor is Associate Professor in the Department of Electronics, SRCASW, University of Delhi and has been actively teaching neural networks and artificial intelligence for the last 20 years. She completed her masters in Electronics in 1996 and PhD in 2011, during PhD she was awarded the prestigious DAAD fellowship to pursue a part of her research work in Karlsruhe Institute of Technology, Karlsruhe, Germany. She was awarded the Best Presentation Award at the Photonics 2008 international conference. She is an active member of ACM, AAAI, IEEE, and INNS. She has authored books in the field of deep learning, artificial intelligence using TensorFlow. She has more than 40 publications in international journals and conferences. Her present research areas include ML, AI, Deep Reinforcement Learning and Robotics.

Books Authored:

• Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems
• TensorFlow Machine Learning Projects: Build 13 real-world projects with advanced numerical computations using the Python ecosystem
• TensorFlow 1.x Deep Learning Cookbook: Over 90 unique recipes to solve artificial-intelligence driven problems with Python