4 ms·

Isn't it good that efforts are duplicated? It commoditizes the work and results, provides more jobs so there are more people who understand this field. It's u

Isn't it good that efforts are duplicated? It commoditizes the work and results, provides more jobs so there are more people who understand this field. It's unlikely each approach will be exactly similar. Similarly, take a look at the deep learning library market: caffe (I think out of Stanford?), tensorflow (google), pytorch (FB + MS)... each has different strengths, but I'm sure glad the pytorch people pushed ahead, even though google put a ton of marketing effort into TF, simply because now we have more awesome things :). Once a market or product is mature, then I can see the "duplicates are wasteful". But a nascent, exploratory field like ML/DL needs as many different approaches as is possible. Now, if only we could gradient descent to find the optimal approach ;).

epmaybe 9y ago
Should I move from theano to tensorflow? I didn't realize that theano was no longer being developed when I first starting playing with keras.
- billysbeanes 9y ago
  Definitely, Theano is no longer active or have plans to be. If you don't need mobile on-device D.L., take a look at pytorch. Otherwise, Tensorflow. Fasi.ai will release some excellent self-paced coursework in January for Pytorch. Best bang for the buck (free, but time ain't) I've seen in any AI learning. Much of the lower level stuff is optimized for you, and he gives some great SOTA tricks for getting in the top 10% in kaggle competitions in like an hour or two. Alas, no pytorch on device yet. But the state of the art is nearly 100% turnover every year, so the question becomes: do you need SOTA? Many problems are 98+% solved these days, so maybe we've reached "good enough" with some of these applications of d.l.
- taneq 9y ago
  Does Theano meet your needs? Then no. Does TensorFlow meet them better, enough to justify the cost in switching? Then yes. "Actively developed" is a silly metric. Focus on features, flexibility, robustness etc.
  - nl 9y ago
    For neural network libraries this isn't sensible. For many (most?) users outside of Google and Facebook the most important feature is "is there an off-the-shelf implementation of new technique XXX or do I have to build it myself?" For most users the sensible choice comes down to Keras+Tensorflow or PyTorch.
    - kirillseva 9y ago
      Depends on what you do. If you're starting a new project picking Theano indeed isn't a very good choice due to the reasons you've mentioned. However, if you already have a stable piece of software that does what you want it to do then migration won't add much value and you could spend this time doing something more important, like improving documentation or having dinner with your family and friends. However it's worth pointing out that theano's API is somewhat similar to tensorflow so migrating shouldn't be too hard and should be fairly easy to test

epmaybe 9y ago

Should I move from theano to tensorflow? I didn't realize that theano was no longer being developed when I first starting playing with keras.

billysbeanes 9y ago
Definitely, Theano is no longer active or have plans to be. If you don't need mobile on-device D.L., take a look at pytorch. Otherwise, Tensorflow. Fasi.ai will release some excellent self-paced coursework in January for Pytorch. Best bang for the buck (free, but time ain't) I've seen in any AI learning. Much of the lower level stuff is optimized for you, and he gives some great SOTA tricks for getting in the top 10% in kaggle competitions in like an hour or two. Alas, no pytorch on device yet. But the state of the art is nearly 100% turnover every year, so the question becomes: do you need SOTA? Many problems are 98+% solved these days, so maybe we've reached "good enough" with some of these applications of d.l.
taneq 9y ago
Does Theano meet your needs? Then no. Does TensorFlow meet them better, enough to justify the cost in switching? Then yes. "Actively developed" is a silly metric. Focus on features, flexibility, robustness etc.
- nl 9y ago
  For neural network libraries this isn't sensible. For many (most?) users outside of Google and Facebook the most important feature is "is there an off-the-shelf implementation of new technique XXX or do I have to build it myself?" For most users the sensible choice comes down to Keras+Tensorflow or PyTorch.
  - kirillseva 9y ago
    Depends on what you do. If you're starting a new project picking Theano indeed isn't a very good choice due to the reasons you've mentioned. However, if you already have a stable piece of software that does what you want it to do then migration won't add much value and you could spend this time doing something more important, like improving documentation or having dinner with your family and friends. However it's worth pointing out that theano's API is somewhat similar to tensorflow so migrating shouldn't be too hard and should be fairly easy to test