Training Neural Networks: Q&A with Ian Goodfellow, Google

Neural networks require considerable time and computational firepower to train. Previously, researchers believed that neural networks were costly to train because gradient descent slows down near local minima or saddle points. At the RE.WORK Deep Learning Summit in San Francisco, Ian Goodfellow, Research Scientist at Google, will challenge that view and look deeper to find the true bottlenecks in neural network training. Before joining the Google team, Ian earned a PhD in machine learning from Université de Montréal, under his advisors Yoshua Bengio and Aaron Courville. During his studies, which were funded by the Google PhD Fellowship in Deep Learning, he wrote Pylearn2, the open source deep learning research library, and introduced a variety of new deep learning algorithms.