Pytorch is an open source machine learning (ML) framework boasting a rich ecosystem of tools and libraries that extend PyTorch and support development in computer vision, NLP and or that supports other ML goals.
Data Scientist Engineer/Researcher in Research & Development at Prewave (Information Services, 51-200 employees)
Chose Pytorch
TensorFlow without Keras is not a pleasant experience; when using Keras, it is pretty nice, but it feels more opinionated than PyTorch; one is less free, which is not an issue in industrial settings with classic workflow but can be an issue in research settings. JAX is great …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose Pytorch
Saving and loading Machine/Deep Learning models is very easy with Pytorch. It provides visualization capabilities when combined with Tensorboard, and mathematical operations are highly optimized. Easy to understand for a person who is an expert in Python. It takes significantly …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose Pytorch
Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose Pytorch
As I described in previous statements, Pytorch is much better suited than TensorFlow from a software development look. This Pythonic idea was then taken and repeated by all the other frameworks.
You can get to better performance models by better understanding the deep learning …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Verified User
Chose Pytorch
The syntax of PyTorch is much better in my opinion, and the programming style is more pythonic and easier to use. I also think PyTorch is a lot easier to debug than the competitors I've listed (Caffe2 and TensorFlow). I do like some of the examples given on tensorflows website, …
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
They have created Pytorch Lightening on top of Pytorch to make the life of Data Scientists easy so that they can use complex models they need with just a few lines of code, so it's becoming popular. As compared to TensorFlow(Keras), where we can create custom neural networks by just adding layers, it's slightly complicated in Pytorch.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The big advantage of PyTorch is how close it is to the algorithm. Oftentimes, it is easier to read Pytorch code than a given paper directly. I particularly like the object-oriented approach in model definition; it makes things very clean and easy to teach to software engineers.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Pytorch is very, very simple compared to TensorFlow. Simple to install, less dependency issues, and very small learning curve. TensorFlow is very much optimised for robust deployment but very complicated to train simple models and play around with the loss functions. It needs a lot of juggling around with the documentation. The research community also prefers PyTorch, so it becomes easy to find solutions to most of the problems. Keras is very simple and good for learning ML / DL. But when going deep into research or building some product that requires a lot of tweaks and experimentation, Keras is not suitable for that. May be good for proving some hypotheses but not good for rigorous experimentation with complex models.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
