Chapter 11. Building Intelligent Applications with Deep Learning and TensorFlow

This June, I spoke with Rajat Monga, who serves as a director of engineering at Google and manages the TensorFlow engineering team. We talked about how he ended up working on deep learning, the current state of TensorFlow, and the applications of deep learning to products at Google and other companies. Here are some highlights from our conversation.

Deep Learning at Google

There’s not going to be too many areas left that run without machine learning that you can program. The data is too much, there’s just too much for humans to handle. … Over the last few years, and this is something we’ve seen at Google, we’ve seen hundreds of products move to deep learning, and gain from that. In some cases, these are products that were actually applying machine learning that had been using traditional methods for a long time and had experts. For example, search, we had hundreds of signals in there, and then we applied deep learning. That was the last two years or so.

For somebody who is not familiar with deep learning, my suggestion would be to start from an example that is closest to your problem, and then try to adapt it to your problem. Start simple; don’t go to very complex things. There are many things you can do, even with simple models.

TensorFlow Makes Deep Learning More Accessible

At Google, I would say there are the machine learning researchers who are pushing machine learning research, ...

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