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I'm pretty sure this already well covered in https://www.tensorflow.org/programmers_guide/ https://www.tensorflow.org/programmers_guide/ I don't think reading

I'm pretty sure this already well covered in https://www.tensorflow.org/programmers_guide/ https://www.tensorflow.org/programmers_guide/ I don't think reading this will give you any understanding of what tensorflow is useful for, or how to do it. The steps of writing a tensorflow program are always like the 'how to draw an owl'; first you define some simple tensors... then just, do the rest of it. Step 1: Define tensors and inputs. OK! Step 2: Linear regression. OK! (useless, but sure) Step 3: Generate high resolution cat videos from a corpus of dog videos. Uh... ???? There are plenty of very good simple introductions to tensorflow. The tensorflow tutorials themselves try to drop people in 'from the top' with high level practical examples, so we're good on that front too. What's missing is a middle ground of 'and then do something practical but simple'. You know what the 'best practice' advice for working with a GAN is? Find someone else's implementation, copy it, and tweek the hyper parameters, change the input. This is why I recommend people learn keras, not tensorflow; because it isn't super production ready and practical, but it will let you learn to build and test models easily. ...and if there's one 'programmers guide' to tensorflow, it's exactly that: You don't just 'build' a tensorflow model; problem solved off you go. Nope, you're going to be going back and tweaking and changing and randomly trying different stuff over and over again until you stumble into a 'good enough' solution to run with. ...and that solution; it might almost work for some other similar domains... but it probably doesn't generalize. You'll probably have to do the whole thing from scratch again. Machine learning. Fun times.

mark_l_watson 9y ago
Good points. One comment: I use GANs at work. While useful they can be difficult to train. Sometimes the loss function for the combined discriminator and generator does not decrease with training as much as you would like, but the generator used on its own is still useful. Goodfellow, inventor of GAN, only spends about one page in his long deep learning book on GANs. RNNs also make good generators and are easier to train. Anyway, GANs may not be good for practical how-to tutorials. Edit: good advice on using Keras. Keras is ‘understandable’ in the sense that reading the code for Keras itself is useful and Francois Chollet, creater of Keras, has a fantastic new book out - which I strongly recommend.

mark_l_watson 9y ago

Good points. One comment: I use GANs at work. While useful they can be difficult to train. Sometimes the loss function for the combined discriminator and generator does not decrease with training as much as you would like, but the generator used on its own is still useful. Goodfellow, inventor of GAN, only spends about one page in his long deep learning book on GANs. RNNs also make good generators and are easier to train. Anyway, GANs may not be good for practical how-to tutorials. Edit: good advice on using Keras. Keras is ‘understandable’ in the sense that reading the code for Keras itself is useful and Francois Chollet, creater of Keras, has a fantastic new book out - which I strongly recommend.