Which Deep Learning Package is Best for Computational Physics?

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In summary, the individual is interested in using deep learning for computational physics but lacks experience in this area. They are seeking recommendations for deep learning packages and have been advised to start by reading books on machine learning and deep learning, such as "The 100 page Book on ML" by Burkiv and "Hands-on book by Geron". These books cover data cleaning, strategies, and core packages in Python and TensorFlow.
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Photonico
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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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Likes berkeman

FAQ: Which Deep Learning Package is Best for Computational Physics?

What deep learning packages are commonly used in computational physics?

Some commonly used deep learning packages in computational physics include TensorFlow, PyTorch, Keras, Theano, and Caffe.

Which deep learning package is best for beginners in computational physics?

For beginners in computational physics, TensorFlow or Keras are often recommended due to their ease of use and comprehensive documentation.

What factors should be considered when choosing a deep learning package for computational physics?

Factors to consider when choosing a deep learning package for computational physics include ease of use, compatibility with existing code, community support, and specific features required for the research.

Are there any performance differences between deep learning packages in computational physics?

While performance differences may exist between deep learning packages, the impact on computational physics tasks may vary. It is recommended to conduct benchmarking tests to determine the best package for specific applications.

Can multiple deep learning packages be used in combination for computational physics tasks?

Yes, multiple deep learning packages can be used in combination for computational physics tasks. This approach, known as transfer learning, can leverage the strengths of different packages for improved performance and efficiency.

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