cs229-2019-summer
SciMLBook
| cs229-2019-summer | SciMLBook | |
|---|---|---|
| 1 | 4 | |
| 151 | 1,796 | |
| - | 1.2% | |
| 0.0 | 4.9 | |
| over 2 years ago | about 1 month ago | |
| HTML | HTML | |
| - | - |
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cs229-2019-summer
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Are there any good books or videos for beginners?
I would usually recommend starting with Stanford's lectures and when you reach Linear regression you can switch to previous year's. I find 2018 lectures to be much more accessible but 2019 presents some basic concepts in the first lectures that are useful if you don't have the background. Alternatively, there is Caltech's Machine Learning Course.
SciMLBook
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SciML Textbook
I've been working on and off using SciML. I just found out they have an e-book: https://book.sciml.ai/
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What's Great about Julia?
I'm hoping the new SciML docs can become a good enough source for beginners looking to do scientific computing (https://docs.sciml.ai/Overview/stable/). It's not there yet, we literally started redirecting links to the new docs on Monday so that's how new it is, it's already moving in the direction of having a lot of materials for new users (in scientific computing specifically, this is not and will not be a general Julia resource) before ever hitting deeper features.
Though if someone wants to dive deep into the language, I'd plug my own SciML course notes: https://book.sciml.ai/, which again is not for general usage but scientific computing but does show a lot about good programming styles (see https://book.sciml.ai/notes/02-Optimizing_Serial_Code/).
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SciML/SciMLBook: Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
This was previously the https://github.com/mitmath/18337 course website, but now in a new iteration of the course it is being reset. To avoid issues like this in the future, we have moved the "book" out to its own repository, https://github.com/SciML/SciMLBook, where it can continue to grow and be hosted separately from the structure of a course. This means it can be something other courses can depend on as well. I am looking for web developers who can help build a nicer webpage for this book, and also for the SciMLBenchmarks.
What are some alternatives?
cs229-2018-autumn - All notes and materials for the CS229: Machine Learning course by Stanford University
18337 - 18.337 - Parallel Computing and Scientific Machine Learning
cs229-solution - CS229 Solution (summer 2019, 2020).
Accessors.jl - Update immutable data
18S096SciML - 18.S096 - Applications of Scientific Machine Learning
Setfield.jl - Update deeply nested immutable structs.
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]
DiffEqFlux.jl - Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
julia - The Julia Programming Language
mamba - The Fast Cross-Platform Package Manager