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Ian J. Goodfellow (born 1985 or 1986) is an American computer scientist, engineer, and executive, most noted for his work on artificial neural networks and deep learning. He was previously employed as a research scientist at Google Brain and director of machine learning at Apple and has made several important contributions to the field of deep ...
Ian Goodfellow Research Scientist San Francisco Bay Area 28K followers 500+ connections Join to view profile DeepMind About I'm an industry leader in machine learning. Activity I am honored to be...
Age: 31 Affiliation: Google Brain Team Ian Goodfellow Invented a way for neural networks to get better by working together. A few years ago, after some heated debate in a Montreal pub, Ian...
Ian Goodfellow and Yoshua Bengio and Aaron Courville Exercises Lectures External Links The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.
Ian Goodfellow @goodfellow_ian · Sep 27, 2022 I enjoyed working with Yao on adversarial examples. She's passionate about teaching and would make a great advisor for anyone who is considering grad school in deep learning right now Quote Tweet Yao Qin @YaoQinUCSD · Sep 27, 2022
Director of machine learning Ian Goodfellow announced his resignation last week, telling colleagues that CEO Tim Cook’s push to get employees back into the office had driven him out. “I believe...
Goodfellow is now a research scientist on the Google Brain team, at the company’s headquarters in Mountain View, California. When I met him there recently, he still seemed surprised by his...
Ian Goodfellow External Links. Google Scholar profile; Deep Learning textbook; General Information. Presentations
Ian Goodfellow, who worked as Apple’s director of machine learning, abruptly resigned in May in response to the company’s return-to-office mandate. Goodfellow joined Google’s DeepMind ...
Generative Adversarial Networks. Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio. We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data ...