How the 10 Worst keras vs pytorch Fails of All Time Could Have Been Prevented
keras is a deep learning framework, whereas pytorch is a C++ library for building neural network models. Both are great tools for developers, but keras is faster and more widely used, and pytorch is much simpler and more accessible to larger teams and organizations.
Keras vs pytorch I don’t think there’s any real comparison, but I suppose it’s a small thing, but I’d like to see more teams working in the keras ecosystem to keep the pace when it comes to deep learning.
To me, keras is the winner because it has really good documentation and great tutorials. This gives us a great foundation, but the developers who are doing the actual deep learning are able to get their hands on the model faster and then work on improving it.
PyTorch is like the other big contender because people seem to have lots of passion for that (maybe the same as Keras?). I mean, for all the hype, the fact that they have a really good documentation and tutorials is a big difference that I could see helping them really get into it.
I think the deep learning models in Keras and PyTorch are pretty similar, but with PyTorch it’s easier to write code and learn from the documentation than with Keras which more often than not is really complicated and difficult to understand. Keras has a lot of other cool features like a neural network scheduler in the form of `.` which can be a good way to learn to write a loop.
pytorch looks like a cool framework from a very simple view, but if you are a beginner, you might feel a little lost when you first start learning the whole things. That’s because pytorch is pretty much a black box. Keras on the other hand, it’s a pretty nice framework, but its still a black box.
Keras looks like a great framework, but at first glance is a black box. It gives you all the tools you need to write a code, but at the same time, you have to get used to the idea that it can be a bit intimidating and you have to make sure that you are learning all the new features at the same time.
pytorch is easier to get into, but it is a black box. Keras gives you a lot of useful tools and a complete framework that you can use to build really sophisticated stuff. Thats what our team is working on in a big collaboration with the keras guys to make Keras. This means that pytorch will be getting lots of updates and will be getting a lot of nice new features. Keras will be the new black box.
pytorch will be getting the same features as keras, and pytorch has been working in that black box for a long time. Keras is getting the same features that pytorch has been working on for a long time. This means that there will be a lot of common code and that pytorch won’t have to change much at all.
Keras and pytorch are two amazing open source neural networks that are used daily by lots of people around the world. But what’s different is keras is going to be more data oriented (and less data centered), while pytorch is going to be more image based. A big part of how pytorch is going to be different is that they’re going to be using a new neural network called TensorFlow.
