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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1. What is a "deep learning package"?

A deep learning package is a software library or framework that provides tools and resources for implementing deep learning algorithms. It typically includes pre-built functions for common tasks such as data preprocessing, model building, and model training.

2. How do I choose the right deep learning package for my project?

Choosing the right deep learning package depends on several factors, such as the type of deep learning task you want to perform, the programming language you are comfortable with, the level of customization you need, and the size of your dataset. It is important to research and compare different packages to find the one that best fits your project's requirements.

3. What are some popular deep learning packages?

Some popular deep learning packages include TensorFlow, Keras, PyTorch, Theano, and Caffe. These packages have a large and active community, extensive documentation, and are widely used in the industry and academia.

4. Are there any free deep learning packages available?

Yes, there are many free and open-source deep learning packages available, such as TensorFlow, Keras, and PyTorch. These packages can be downloaded and used for free, and their source code is publicly available for anyone to modify and contribute to.

5. Is it necessary to have a strong background in mathematics to use a deep learning package?

While a basic understanding of mathematics, specifically linear algebra and calculus, can be helpful in understanding the inner workings of deep learning algorithms, it is not necessary to have a strong background in mathematics to use a deep learning package. Many packages provide high-level APIs that abstract away the mathematical complexity and allow users to focus on implementing their ideas.

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