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Thank you so much Francois! I'm incredibly excited about this release!
What should I read to start grokking this kind of things? I feel quite ready to go full "DIY math PhD" mode and consume some heavy reading if necessary, but where do I even start?
I love Keras but I think this update broke more things than you realized. For example it's no longer possible to get the validation set score (val_acc) during training which renders early stopping impossible. This was a documented feature on your FAQ. Is the old documentation still available? I'd like to wait before I upgrade. Edit:typo
I'm only starting with all that machine-learning, NN stuff and as many others I want to ask for some guidance/resources/learning material. What I feel especially lacking is something very broad and generic, some overview of existing techniques (but not as naïve as Ng's ML course, I assume). There exist a lot of estimators and classifiers, there exist a lot of techniques and tricks to train models, there exist a lot o…
Will Keras2 support PyTorch as backend, in the future? Answer: [0] No, there are no plans to support PyTorch. There is nothing to be gained in supporting every novelty framework that crops up every quarter. Our goal is to make deep learning accessible and useful to as many people as possible, and that goal is completely opposite to building up deep learning hipster cred. [0]: https://github.com/fchollet/keras/issues/…
I'm only starting with all that machine-learning, NN stuff and as many others I want to ask for some guidance/resources/learning material. What I feel especially lacking is something very broad and generic, some overview of existing techniques (but not as naïve as Ng's ML course, I assume). There exist a lot of estimators and classifiers, there exist a lot of techniques and tricks to train models, there exist a lot o…
The most common way to apply machine learning is supervised classification. The basic formula is: we learn a model (set of weights) to approximately map data (a matrix X) to corresponding labels (a matrix Y). Where you can use logistic regression to learn a set of weights, you can use a keras-based neural network.
If all of that makes sense to you already, I think you're well prepared to read Keras' documentation.
I'm only starting with all that machine-learning, NN stuff and as many others I want to ask for some guidance/resources/learning material. What I feel especially lacking is something very broad and generic, some overview of existing techniques (but not as naïve as Ng's ML course, I assume). There exist a lot of estimators and classifiers, there exist a lot of techniques and tricks to train models, there exist a lot o…
Nothing beats reading papers. Check this out for a very comprehensive list of the most influential deep learning papers: https://github.com/songrotek/Deep-Learning-Papers-Reading-Ro...
2. It's still an input->output paradigm, rather than a {input, output}->loss paradigm which gives more flexibility.
These two issues are the main reason why I stick to slightly lower level APIs, even though I _want_ to use Keras.
I'm only starting with all that machine-learning, NN stuff and as many others I want to ask for some guidance/resources/learning material. What I feel especially lacking is something very broad and generic, some overview of existing techniques (but not as naïve as Ng's ML course, I assume). There exist a lot of estimators and classifiers, there exist a lot of techniques and tricks to train models, there exist a lot o…
I'd definitely watch the first few episodes of Ng's stuff, up to and including logistic regression (unless you know all of that already, in which case: read papers and do practice projects for yourself--or compete in kaggle if you don't have any application ideas) The most common way to apply machine learning is supervised classification. The basic formula is: we learn a model (set of weights) to approximately map da…
1. Still no support for multiple losses. Models like VAEs cannot be idiomatically implemented. The second loss has to be 'hacked' in. Notice how in the official example for VAE, the kl_loss is computed using variables which are NOT available via the loss function ( https://github.com/fchollet/keras/blob/master/examples/varia... ) 2. It's still an input->output paradigm, rather than a {input, output}->loss paradigm wh…