JLD2.jl
NumPy
JLD2.jl | NumPy | |
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2 | 272 | |
525 | 26,413 | |
1.7% | 1.1% | |
8.1 | 10.0 | |
9 days ago | 4 days ago | |
Julia | Python | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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JLD2.jl
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
First, you need to save the model from the notebook to a file. For this you can use JLD2.jl module. This module used to serialize Julia object to HDF5-compatible format (which is well known by Python data scientists) and save it to a file.
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Best format to save matrices to a text file? (R interop)
I didn't realize this was the Julia subreddit. HDF5 or multiple CSV files would be my suggestion. As a side note, check out the JLD2 package. It's a HDF5 compatible format where the package is written in pure Julia.
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?
julia - The Julia Programming Language
SymPy - A computer algebra system written in pure Python
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
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
Plots.jl - Powerful convenience for Julia visualizations and data analysis
blaze - NumPy and Pandas interface to Big Data
Rocket.jl - Functional reactive programming extensions library for Julia
SciPy - SciPy library main repository
StatsWithJuliaBook
Numba - NumPy aware dynamic Python compiler using LLVM
PlotDocs.jl - Documentation for Plots.jl
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).