4 ms·

I find the documentation daunting but I had assumed that was because I didn't really understand how AI works and that as my understanding grew the docs would st

I find the documentation daunting but I had assumed that was because I didn't really understand how AI works and that as my understanding grew the docs would start to make more sense.

0xbear 9y ago
Take a look at PyTorch. Much easier to understand, fewer bugs, much less obscure all around. I've spent years working with TF and have come to dislike its inflexible, verbose, and convoluted way of doing things. I'm 80% on PyTorch now.
- Q6T46nT668w6i3m 9y ago
  I like and use both PyTorch and TensorFlow. You’re entirely correct that TensorFlow is far more verbose than PyTorch, but Keras remedies this (for many problems). I’d also add that PyTorch has a narrower scope than PyTorch and because of this introduces problems when scaling or distributing (PyTorch’s flexible broadcasting isn’t without trade-offs). Maybe there’ll be a convergence of Caffe 2 and PyTorch to remedy that problem.
  - dr1337 9y ago
    For me, Keras is the least verbose of Tensorflow and PyTorch, however I find it too sparse and there's too much "magic" happening under the nicely done API. I have found PyTorch to be the happy half-way point between Tensorflow and Keras in terms of verbosity.
    - Q6T46nT668w6i3m 9y ago
      I’m curious, what parts do you find too magical? I’ve been trying to help with this problem (clarifying or surfacing features that are too magical without relinquishing Keras’ commitment to “convention over configuration”), so your (or others) feedback is welcome!
- mamp 9y ago
  > I've spent years working with TF Ha, TF was only released in Nov 2015 but sitting around waiting for networks to finish training makes it feel longer than that!
  - 0xbear 9y ago
    I worked with it _before_ it was released, and with DistBelief before that. For a long time I considered TF to be the best option for the class of problems I work on. I was also extremely skeptical of PyTorch, but it won me over. Now that I'm no longer on a steady diet of Google Kool-Aid, I have the luxury of picking the best tool for the job. For a number of things that tool is made by Google (protocol buffers, gtest, gmock, grpc, some other things). But not for ML.
    - mamp 9y ago
      Based on your experience I'll definitely give PyTorch a go. Thanks.

I find the documentation daunting but I had assumed that was because I didn't really understand how AI works and that as my understanding grew the docs would start to make more sense.

0xbear 9y ago

Take a look at PyTorch. Much easier to understand, fewer bugs, much less obscure all around. I've spent years working with TF and have come to dislike its inflexible, verbose, and convoluted way of doing things. I'm 80% on PyTorch now.

Q6T46nT668w6i3m 9y ago
I like and use both PyTorch and TensorFlow. You’re entirely correct that TensorFlow is far more verbose than PyTorch, but Keras remedies this (for many problems). I’d also add that PyTorch has a narrower scope than PyTorch and because of this introduces problems when scaling or distributing (PyTorch’s flexible broadcasting isn’t without trade-offs). Maybe there’ll be a convergence of Caffe 2 and PyTorch to remedy that problem.
- dr1337 9y ago
  For me, Keras is the least verbose of Tensorflow and PyTorch, however I find it too sparse and there's too much "magic" happening under the nicely done API. I have found PyTorch to be the happy half-way point between Tensorflow and Keras in terms of verbosity.
  - Q6T46nT668w6i3m 9y ago
    I’m curious, what parts do you find too magical? I’ve been trying to help with this problem (clarifying or surfacing features that are too magical without relinquishing Keras’ commitment to “convention over configuration”), so your (or others) feedback is welcome!
mamp 9y ago
> I've spent years working with TF Ha, TF was only released in Nov 2015 but sitting around waiting for networks to finish training makes it feel longer than that!
- 0xbear 9y ago
  I worked with it _before_ it was released, and with DistBelief before that. For a long time I considered TF to be the best option for the class of problems I work on. I was also extremely skeptical of PyTorch, but it won me over. Now that I'm no longer on a steady diet of Google Kool-Aid, I have the luxury of picking the best tool for the job. For a number of things that tool is made by Google (protocol buffers, gtest, gmock, grpc, some other things). But not for ML.
  - mamp 9y ago
    Based on your experience I'll definitely give PyTorch a go. Thanks.