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This has been my experience as well :). Deep learning is a lot of random guesswork, trial and error. I am almost always in the 'brute force' mode. However, in t
by deepGem 9y ago
This has been my experience as well :). Deep learning is a lot of random guesswork, trial and error. I am almost always in the 'brute force' mode. However, in this course, you learn more about the fundamentals of convolutions and backprop. You have to implement your own backprop - not sure of what use that is, given that it's a one line code in TensorFlow
- amelius 9y agoStudying how to write your own back propagation algorithm could be useful if you're a deep learning researcher. But for most people it would be like studying semiconductor physics if you only want to write software.
- speedplane 9y agoI watched a great video on Tensorflow (link below). It mostly introduces very basic deep learning concepts, but there are a few key moments in the 2 hour+ video, where he explains what to do if something goes wrong. It's definitely not a "science", but with enough experience in deep learning, you can intuit what's going on inside the black box, and there are best practices on what to try next. For example, he goes through a few examples where a neural net has too many weights, or too little data or improperly connected nodes. All three result in problems, but the problems exhibit themselves in slightly different ways and with expertise you can start identifying them. https://www.youtube.com/watch?v=vq2nnJ4g6N0 https://www.youtube.com/watch?v=vq2nnJ4g6N0