AI and Machine Learning for Coders

Foreword

Dear Reader,

AI is poised to transform every industry, but almost every AI application needs to be customized for its particular use. A system for reading medical records is different from one for finding defects in a factory, which is different from a product recommendation engine. For AI to reach its full potential, engineers need tools that can help them adapt the amazing capabilities available to the millions of concrete problems we wish to solve.

When I led the Google Brain team, we started to build the C++ precursor to TensorFlow called DistBelief. We were excited about the potential of harnessing thousands of CPUs to train a neural network (for instance, using 16,000 CPUs to train a cat detector on unlabeled YouTube videos). How far deep learning has come since then! What was once cutting-edge can now be done for around $3,000 of cloud computing credits, and Google routinely trains neural networks using TPUs and GPUs at a scale that was unimaginable just years ago.

TensorFlow, too, has come a long way. It is far more usable than what we had in the early days, and has rich features ranging from modeling, to using pretrained models, to deploying on low-compute edge devices. It is today empowering hundreds of thousands of developers to build their own deep learning models.

Laurence Moroney, as Google’s lead AI Advocate, has been a major force in building TensorFlow into one of the world’s leading AI frameworks. I was privileged to support his teaching TensorFlow ...