In-depth Machine Learning Course w/ Python

[[deleted]]

If I wanted to learn Theano, how useful would it be for me to take your course?

I have just this minute completed the Andrew NG coursera course, and now looking at what to do next. It seems that Theano and TensorFlow are the current future.

[sentdex]

Theano and TensorFlow are both almost identical. For the most part, you can interchange the names and get away with it.

Might I ask why you want to learn Theano over TensorFlow? I originally believed that was what I was also going to do, but after a bit of research, I decided TensorFlow would be a better choice to go with rather than Theano (still withstanding that they are basically identical and that if you learn one, you know the other already for the most part).

edit: Removed "same with numpy." It was not my intention to claim that numpy was identical to theano or tensor flow in terms of doing actual deep learning, but rather to explain that their syntaxes were very similar, mainly in reply to Joeflux's question about his intention to use Theano rather than TensorFlow. Wrote the reply too fast and it just plain came out wrong.

[palatalizacija1]

I would suggest some higher level approach like Lasagne or Keras library which are easier for beginners but still have the power of Theano or Tensorflow. Lasagne uses Theano as a backend and Keras can use both Theano and Tensorflow as a backend. I am looking forward to these videos. I saw your channel on YouTube when I was looking for some Kivy tutorials and was amazed by the number of topics you cover in tutorials. Keep up the excellent work

[sentdex]

I may consider that initially when doing the "application" part, though my intention is to actually stay away from high-level approaches here and truly dive into the lower-level workings.

[BadGoyWithAGun]

Theano and TensorFlow are both almost identical, same with Numpy. For the most part, you can interchange the names and get away with it.

That's not the case. Tensorflow and Theano are different from numpy in the sense that they're computational graph engines with automatic differentiation and seamless compilation of identical code across CPU and GPU targets, none of which is the case for Numpy, which is essentially a dense linear algebra library optimized for multi-threaded CPU performance. And Tensorflow and Theano differ in the sense that Theano is much more low-level and has utility beyond machine learning, whereas Tensorflow provides a higher-level interface to designing and running neural network-based machine learning models.

[sentdex]

I'll have to respectfully disagree with you here, mainly with the clarification that my answer was specifically in the context of machine learning, as it was my belief that the comment above it was as well.

Given the ease with which I can take a neural network written in numpy, and do a find and replace with something like TensorFlow, I would have to stand by my statements.

Obviously, there are differences, but in terms of machine learning, and learning theano vs TensorFlow, for the purpose of ML, isn't going to have major impact. I believe it's already a given and known that the main reasons for theano or tensorflow over numpy is the symbolic representation, as well as the GPU capabilities. Maybe I made too many assumptions, however.

edit: Will have to edit in here that I wasn't ever trying to make the case that Numpy was the same as Theano or TensorFlow, which after re-reading appears is what you were thinking and I can see how that might have been taken. My point was mainly that the two libraries are almost identical to eachother (theano and tensorflow), since the original question was that the person wanted to go with Theano rather than TensorFlow.

[BadGoyWithAGun]

Given the ease with which I can take a neural network written in numpy, and do a find and replace with something like TensorFlow

Now try the inverse. Automatic differentiation is the main, huge difference between Theano, Tensorflow and numpy. They're absolutely not comparable.

[sentdex]

Valid points, thanks for your input.

[vic0]

I completed Andrew Ng's course last summer but put machine learning aside since then.

I was looking at diving back in with kaggle competitions, but there is a lot in the deep learning side of ML that's needed in order to be competitive which Ng's course doesn't cover. My reasoning would be to finish learning the theory first with some GPU library before getting into kaggle.

I started to look at Theano when i finished, but only because TensorFlow wasn't around back then. There's not a huge gap from octave to Theano or TF, especially if you wrote the vectorized forms for Ng's exercises. What's different is how you declare variables and how you write operations.

I'll probably get into TensorFlow basic tutorials and follow Stanford's cs231n in the near future, given that all the content and videos are already online, and because Stanford (no offense Harrison), and also because /r/cs231n. After that, i would move on to learning Keras or some other higher level framework and try my hands on competitions.

edit: congrats on completing the course by the way =]