That may mean you'll want to run your training in the cloud: AWS, Azure, and Bluemix all offer instances with GPUs as of this writing, as will Google early in 2017.
While the biggest cloud GPU instances can cost $14 per hour to run, there are less expensive alternatives. An AWS instance with a single GPU can cost less than $1 per hour to run, and the Azure Batch Shipyard and its deep learning recipes using the NC series of GPU-enabled instances run your training in a compute pool, with the small NC6 instances going for 90 cents an hour.
Yes, you can and should install your deep learning package of choice on your own computer for learning purposes, whether or not it has a suitable GPU. But when it comes time to train models at scale, you probably won't want to limit yourself to the hardware you happen to have on site.
For deeper learning
You can learn a lot about deep learning simply by installing one of the deep learning packages, trying out its samples, and reading its tutorials. For more depth, consider one or more of the following resources:
- Neural Networks and Deep Learning, by Michael Nielsen
- A Brief Introduction to Neural Networks, by David Kriesel
- Deep Learning, by Yoshua Bengio, Ian Goodfellow, and Aaron Courville
- A Course in Machine Learning, by Hal Daumé III
- The TensorFlow Playground, by Daniel Smilkov and Shan Carter
- Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition
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