cs229-2019-summer
18S096SciML
| cs229-2019-summer | 18S096SciML | |
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| 1 | 1 | |
| 151 | 303 | |
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| 0.0 | 2.7 | |
| over 2 years ago | about 2 years 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.
18S096SciML
What are some alternatives?
cs229-2018-autumn - All notes and materials for the CS229: Machine Learning course by Stanford University
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
cs229-solution - CS229 Solution (summer 2019, 2020).
SciMLBook - Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)
SciMLBenchmarks.jl - Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
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