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SciMLBook Alternatives
Similar projects and alternatives to SciMLBook
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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cs229-2019-summer
All notes and materials for the CS229: Machine Learning course by Stanford University
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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SciMLTutorials.jl
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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DiffEqSensitivity.jl
Discontinued 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]
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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
SciMLBook reviews and mentions
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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.
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A note from our sponsor - InfluxDB
www.influxdata.com | 10 May 2024
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