Deep Learning for Image Recognition in Astrophysics

AI Thread Summary
The discussion centers on seeking recommendations for books on deep learning and image recognition, specifically for applications in astrophysics. A notable suggestion is "Neural Networks and Deep Learning" by Michael Nielsen, which is available for free online and includes downloadable software and test datasets for practical application. Participants express appreciation for this resource, highlighting its effectiveness in understanding deep learning concepts. Additionally, a series of videos by Grant Sanderson on the 3Blue1Brown channel is recommended for its clarity and educational value in explaining neural networks. Overall, the focus is on accessible resources that can enhance understanding and application of deep learning in the context of astrophysics.
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Summary:: I'm looking for some great books on deep learning related to image recognition that I can use in astrophysics.

Hello,
I'm about to start my master thesis, where I, in short, will be comparing snapshots of young binary stars from simulations to observations using deep learning - basically, image recognition. I'll be starting in a few months and I want to read as much as possible before I actually start. So, I'm looking for some great books on machine learning, deep learning and image recognition, and would like to hear if there are any that you can recommend?

Thanks in advance!
 
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anorlunda said:
http://neuralnetworksanddeeplearning.com/

That book by Michael Nielsen is free, online, and you can download the software and test datasets to practice it yourself. Hard to do better than that.
This looks amazing. Thank you so much for sharing this! :)
 
I worked all the way through that book. Definitely worth the effort.

I would also highly recommend this relatively short series of Neural Networks videos by Grant Sanderson on his 3Blue1Brown channel. The knowledge that I gained in those videos gave me a much better understanding of how they work than anything else that I've seen.
 
The book is fascinating. If your education includes a typical math degree curriculum, with Lebesgue integration, functional analysis, etc, it teaches QFT with only a passing acquaintance of ordinary QM you would get at HS. However, I would read Lenny Susskind's book on QM first. Purchased a copy straight away, but it will not arrive until the end of December; however, Scribd has a PDF I am now studying. The first part introduces distribution theory (and other related concepts), which...
I've gone through the Standard turbulence textbooks such as Pope's Turbulent Flows and Wilcox' Turbulent modelling for CFD which mostly Covers RANS and the closure models. I want to jump more into DNS but most of the work i've been able to come across is too "practical" and not much explanation of the theory behind it. I wonder if there is a book that takes a theoretical approach to Turbulence starting from the full Navier Stokes Equations and developing from there, instead of jumping from...

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