TensorFlow for poets – O’Reilly Media
When I first started investigating the world of deep learning, I found it very hard to get started. There wasn’t much documentation, and what existed was aimed at academic researchers who already knew a lot of the jargon and background. Thankfully, that has changed over the last few years, with a lot more guides and tutorials appearing.
I always loved EC2 for Poets, though, and I haven’t seen anything for deep learning that’s aimed at as wide an audience. EC2 for Poets is an explanation of cloud computing that removes a lot of the unnecessary mystery by walking anyone with basic computing knowledge step-by-step through building a simple application on the platform. In the same spirit, I want to show how anyone with a Mac laptop and the ability to use the Terminal can create their own image classifier using TensorFlow, without having to do any coding.
I feel very lucky to be a part of building TensorFlow because it’s a great opportunity to bring the power of deep learning to a mass audience. I look around and see so many applications that could benefit from the technology by understanding the images, speech, or text their users enter. The frustrating part is that deep learning is still seen as a very hard topic for product engineers to grasp. That’s true at the cutting edge of research, but otherwise it’s mostly a holdover from the early days. There’s already a lot of great documentation on the TensorFlow site, but to demonstrate how easy it can be for general software engineers to pick up, I’m going to present a walk-through that takes you from a clean OS X laptop all the way to classifying your own categories of images. You’ll find written instructions in this post, along with a screencast showing exactly what I’m doing.