I didn't realize Keras was actually released before Tensorflow, huh. I used Theano quite a bit in 2014 and early 2015, but then went a couple years without any ML work. Compared to the modern libraries Theano is clunky, but it taught one a bit more about the models, heh.

Wow that gives me flashbacks to learning Theano/Lasagne, which was a breath of fresh air coming from Caffe. Crazy how far we've come since then.

Of course, it's easy to be ideological and defend technology A or B nowadays, but I agree 100% that in 2016/2016 Keras was the first touchpoint of several people and companies with Deep Learning.

The ecosystem, roughly speaking was: * Theano: Verbosity nightmare * Torch: Not-user friendly * Lasagne: A complex attraction on top of Theano. * Caffe: No flexibility at all, anything not the traditional architectures would be hard to implement * Tensor Flow: Unnecessarily complex API and no debuggability

I do not say that Keras solved all those things right away, but honestly, until just the fact that you could implement some Deep Learning architecture in 2017 on top of Keras I believe was one of the critical moments in Deep Learning history.

Of course today people have different preferences and I understand why PyTorch had its leap, but Keras was in my opinion the best piece of software back in the day to work with Deep Learning.

And PyTorch was a miracle after coming from LuaTorch (or Torch7 iirc). We’ve made a lot of strides over the years.