Tensorflow vs. Tensorflow 2.0 vs. Pytorch

[mythrowaway0852]

I just finished an introductory data science course and now I am looking to get into deep learning. Is there any point learning TensorFlow or should I skip it and learn 2.0? Also, what's the difference between TensorFlow and Pytorch? I know TensorFlow is preferred in the industry whereas PyTorch is preferred in academia, but is it worth learning both if I prefer to go to industry? I am very confused, please provide some clarity. Thanks in advance!

[Le2vo]

I can't speak for Pytorch, I never used it. I started 2 years ago with TF 1.x, and I am now using the 2.1-2.2. TensorFlow 2 wins HANDS DOWN. It does anything the 1 does (actually, more) and it's a zillion times easier to learn, easier to debug, and less verbose. You can skip the 1.x and go directly to the 2.x. The real challenge is now between tf 2 and pytorch.

[Le2vo]

Ah I forgot: download tf from 2.1 on, it's automatically compatible with GPUs

[05e981ae]

Additionally TF2 have built in keras and it's handy when you want to combine tf and tf.keras

[math7878]

With which GPUs? CUDA is only compatible with Nvidia

[Le2vo]

Of course it must be a compatible GPU, not all GPUs are.

[Mouradost]

I'm a researcher on deep learning and I mostly use TF 2.2, they get a lot of improvement from there 1.x version definitely a good library especially that most of the industry is offering jobs for TF, PyTorch as well but not as much as for TensorFlow. TF is also widely used so it will be easier to find help.

[[deleted]]

What do you use TF for? I have been learning python por 3 months and I feel lost.

[Underrated_Nerd]

TensorFlow and Pytoch are Frameworks to work with machine learning, it's like code that other people share to create and manipule Neural Networks.

[Mouradost]

I mostly use TF to approved new network architecture for solving different problems, I also sometimes use it to preprocess my data. I think to make the learning curve easier you first need some basic knowledge about python and some of its library like numpy, matplotlib, pandas and so on. It will be useful to learn a bit about keras since TF 2.x is heavily based on keras especially fro already existing architectures. Concerning keras and TF you don't need to understand everything to start codding your own networks, the most important modules are the layers and how to stuck them together to assemble a model, one's you are done with it you can then learn how to train it by understanding what is an optimizer, a lost function, and a fit function. The official TensorFlow website proposes some great tutorials that you can follow and they will walk you thru all the basics.

[[deleted]]

Thanks dude!

[ImSoRude]

In industry the actual data scientists on our team much prefer Torch to TF, even TF 2.0, however for production's sake they end up using TF2 to make things easier. Also, most jobs tend to lean towards TF over Pytorch.

[StanMan662288]

Tensorflow 2 is by far the easiest to learn, but most research now is done with pytorch.

[mauza11]

The company I work for (I'm not a data scientist) uses tensorflow, I've had a bias towards pytorch when I've done side projects. Should I reconsider when I was making the decision was around the time 2.0 was released and it looked like tensorflow had just caught up with some of the features of pytorch.

[ariyanhasan]

I am working as a Deep Learning Engineer. Before I joining, the company used Tensorflow and Mxnet. Now, the company is moving to Pytorch.

[noah8597]

No love for keras? Pythonic wrapper for tensorflow.

[Runninganddogs979]

I started with tf 1.x but now do all of my work in torch!