Knet.jl
NumPy
| Knet.jl | NumPy | |
|---|---|---|
| 1 | 272 | |
| 1,418 | 26,413 | |
| - | 1.1% | |
| 0.0 | 10.0 | |
| almost 2 years ago | 3 days ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Knet.jl
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Should you learn Julia or Python for Machine Learning?
We used to use the popular Flux, Knet, MLBase, and Plots packages for Machine Learning in 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?
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
SymPy - A computer algebra system written in pure Python
machine-learning-roadmap - A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
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
ThreeBodyBot - Poorly written code that generates moderately exciting plots of a very specific physics phenomenon that enthralls dozens of us around the globe.
blaze - NumPy and Pandas interface to Big Data
edward2 - A simple probabilistic programming language.
SciPy - SciPy library main repository
MLBase.jl - A set of functions to support the development of machine learning algorithms
Numba - NumPy aware dynamic Python compiler using LLVM
tensorflow - An Open Source Machine Learning Framework for Everyone
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).