Which Deep Learning Package is Best for Computational Physics?

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The discussion centers on the application of deep learning in computational physics, particularly for those new to the field. A participant expresses interest in utilizing deep learning despite lacking experience, prompting inquiries about suitable deep learning packages. Recommendations include popular frameworks such as PyTorch and TensorFlow. Additionally, participants suggest foundational reading materials to build knowledge in machine learning (ML) and deep learning (DL), specifically highlighting "The 100 Page Book on ML" by Burkiv and "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Geron. These resources emphasize essential concepts, data cleaning, and practical project templates, making them valuable for beginners in the field.
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Hi mates,

I am working in computational physics for condensed matter. I have noticed that there are already some articles using deep learning for computational physics. I want to try this method but I do not have any experience with deep learning or machine learning. The first question is that there are many packages for deep learning, such as PyTorch, TensorFlow, and MxNet. Could I get some recommendations about the choice of deep learning packages?Lu
 
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It would seem you really need to step back a bit and read a couple of books on ML and DL.

The 100 page Book on ML by Burkiv is a good start as is the Hands-on book by Geron

http://themlbook.com/

https://www.amazon.com/dp/1098125975/?tag=pfamazon01-20

The hands-on book has a project template at the end and talks about cleaning your data which is an important aspect of ML and DL.

Both books cover the various strategies and the core packages in Python and Tensorflow.
 
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