Deep Learning has transformed many important tasks, including speech and image recognition. Deep Learning systems scale well by absorbing huge amounts of data to create accurate models. The computational resources afforded by GPUs have been instrumental to this scaling. However, as Deep Learning has become more mainstream, it has generated some hype, and has been linked to everything from world peace to evil killer robots. In this talk, Dr. Ng will help separate hype from reality, and discuss potential ways that Deep Learning technologies can benefit society in the short and long term.
(Phys.org) – A Google team has worked out a neural network approach to transcribe house numbers from Street View images, reading those house numbers and matching them to their geolocation. Google Street View has the user advantage of allowing the user to advance to street level to see the area of interest in detail. Google’s accomplishment in automation is impressive both in the scope of the task involved and the way in which it was done. Consider that Google’s Street View cameras have recorded massive numbers of panoramic images carrying massive numbers of house numbers. “We can for example transcribe all the views we have of street numbers in France in less than an hour using our Google infrastructure,” said the researchers, who have authored the paper, “Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks.” Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, Vinay Shet are the authors. The team used a neural network that contains 11 levels of neurons trained to spot numbers in images. The researchers describe the network as “a deep convolutional neural network that operates directly on the image pixels.” They said they used the DistBelief implementation of deep neural networks to train large, distributed neural networks on high quality images. (More information “Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks“)