Learn how to automate your systems, how to build chat bots and the future of deep learning. Why deep neural nets cannot ever match biological intelligence and nyu investment conference 2017 to do about it? Real-world Machine Learning: A View From the Trenches!

You don’t need to complete this! Something went wrong while submitting the form. Machine and Deep Learning Fundamentals, implementation and new modelling strategies for your frameworks straight from the labs. Leverage conversational architecture with bot-builders and a slick UX. Discover the applications of AI from startups and industry, what it takes to manage an AI company and the impacts on society with this technology.

He currently leads the Google Brain group in Montreal. His main area of expertise is deep learning. His previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models. More broadly, he is interested in applications of deep learning to generative modeling, reinforcement learning, meta-learning, natural language processing and computer vision. Ian Goodfellow is a staff research scientist at Google Brain. He is generally interested in all things deep learning, and usually focuses on generative models, machine learning security, and differential privacy.

Ruslan Salakhutdinov is a UPMC professor of Computer Science in the Machine Learning Department at Carnegie Mellon University. Ruslan’s primary interests lie in deep learning, machine learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data. He was most recently a professor at Northwestern University and a principal applied scientist at Microsoft. From 2013 to 2016, she was an Assistant Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. I currently lead the Google Brain group in Montreal. My main area of expertise is deep learning.

My previous work includes unsupervised pretraining with autoencoders, denoising autoencoders, visual attention-based classification, neural autoregressive distribution models. More broadly, I’m interested in applications of deep learning to generative modeling, reinforcement learning, meta-learning, natural language processing and computer vision. Matthieu Boussard has more than 10 years of experience in different fields of AI. In 2015, he helped start craft ai, the AI startup where he is currently Lead Scientist, working on Machine Learning with whitebox constraints. He received his PhD from the Caen University in France on multi-robot coordination. He received his PhD in machine learning from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Department of Computer Science and Department of Statistics.