You-Dont-Know-JS
Numba
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You-Dont-Know-JS | Numba | |
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301 | 124 | |
175,782 | 9,350 | |
- | 1.7% | |
4.8 | 9.9 | |
about 1 month ago | 6 days ago | |
Python | ||
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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You-Dont-Know-JS
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🧙‍♂️Master JavaScript with these 5 GitHub repositories🪄✨🚀
3. You-Dont-Know-JS
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Eloquent JavaScript 4th edition (2024)
There are 6 books, the author recommends reading them in an order:
https://github.com/getify/You-Dont-Know-JS?tab=readme-ov-fil...
If the second edition is not available, you can read the first edition, just be aware some small things may be slightly out of date.
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10 GitHub repositories that every developer must follow
âś… getify/You-Dont-Know-JS : https://github.com/getify/You-Dont-Know-JS
- 18 Must-Bookmark GitHub Repositories Every Developer Should Know
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Coming to grips with JS: a Rubyist's deep dive
You Don't Know JS
- Ask HN: Best books to learn web development?
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Best way to re-learn JavaScript as a former senior level js dev?
Kyle Simpson, the guy who wrote the YDKJS series https://github.com/getify/You-Dont-Know-JS has classes on there and they’re honestly the shit just like his books.
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[AskJS] What would be a more recent equivalent to Crockford's "Good Parts" ?
In any case, maybe You Don't Know JS series could be it. It is a series of books. All of the books are pretty short. You can get it for free at the link or buy them on amazon.
You Don't Know JS as noted above.
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So I wrote a Spanish textbook
Great effort! Consider putting the book directly into your repository, similar to You Don't Know Javascript, for increased usability and to make it easier for the community to contribute (if that's something you want).
Numba
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Mojo🔥: Head -to-Head with Python and Numba
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.
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Is anyone using PyPy for real work?
Simulations are, at least in my experience, numba’s [0] wheelhouse.
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Python Algotrading with Machine Learning
A super-fast backtesting engine built in NumPy and accelerated with Numba.
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PYTHON vs OCTAVE for Matlab alternative
Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.
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Codon: Python Compiler
Just for reference,
* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."
* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.
* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."
* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."
* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"
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Two-tier programming language
Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.
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Been using Python for 3 years, never used a Class.
There are also just-in-time compilers available for some Python features, that compile those parts to machine code. That includes Numba (usable as a library within CPython) and Pypy (an alternative Python implementation that includes a JIT compiler to improve performance). There’s also Cython, which is a superset of Python that allows more directly interfacing with C and C++ functions, and compiling the resulting combined code.
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Is there a language with lisp syntax but C semantics?
this was a submission from u/bpecsek and shows that lisp with sbcl can do quite well on bench-marking. but keep in mind that these sort of benchmarks can't tell you much about real world applications. moreover if you are really concerned about niche performance you need to start thinking about compilers. heck with an appropriate compiler even python can go wrooom
- [D] Yann LeCun's Hot Take about programming languages for ML
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Python Developer Seeking Input: Is it Worth Learning Rust for FFI?
- if no purpose built libraries are faster, use numba (http://numba.pydata.org/) to speed up your code. Optionally you can also use Taichi (https://www.taichi-lang.org/) instead of numba.
What are some alternatives?
NetworkX - Network Analysis in Python
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Dask - Parallel computing with task scheduling
cupy - NumPy & SciPy for GPU
Pyjion - Pyjion - A JIT for Python based upon CoreCLR
SymPy - A computer algebra system written in pure Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python
Crafting Interpreters - Repository for the book "Crafting Interpreters"
cudf - cuDF - GPU DataFrame Library
julia - The Julia Programming Language
PyMC - Bayesian Modeling and Probabilistic Programming in Python
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows