DoubleFloats.jl
NumPy
DoubleFloats.jl | NumPy | |
---|---|---|
1 | 272 | |
144 | 26,413 | |
4.2% | 1.1% | |
8.0 | 10.0 | |
6 days ago | 7 days ago | |
Julia | Python | |
MIT License | GNU General Public License v3.0 or later |
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DoubleFloats.jl
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The Right Way to Compare Floats in Python
https://github.com/JuliaMath/DoubleFloats.jl
Of course, if you are calling BLAS/LAPACK, you are constrained to use floats, but the recommendation on DoubleFloats is clear: if you know you algorithms, use the increased precision only in the parts that matter
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
FFTW.jl - Julia bindings to the FFTW library for fast Fourier transforms
SymPy - A computer algebra system written in pure Python
SpecialFunctions.jl - Special mathematical functions in Julia
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
game-engine-2d - Planimeter Game Engine 2D - LÖVE-based game engine for Lua
blaze - NumPy and Pandas interface to Big Data
LogarithmicNumbers.jl - A logarithmic number system for Julia.
SciPy - SciPy library main repository
mlscorecheck - Testing the consistency of binary classification performance scores reported in papers
Numba - NumPy aware dynamic Python compiler using LLVM
Hecke.jl - Computational algebraic number theory
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).