NumPy
Keras
| NumPy | Keras | |
|---|---|---|
| 310 | 89 | |
| 31,038 | 63,648 | |
| 1.0% | 0.2% | |
| 10.0 | 9.8 | |
| 4 days ago | 4 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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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.
NumPy
- Python is not a great language for data science. Part 1: The experience
- Choosing Tech Stack in 2025: A Practical Guide
Unmatched integration with ML/AI ecosystems through NumPy, TensorFlow, and PyTorch
- What Dynamic Typing Is For
- Bringing NumPy's type-completeness score to nearly 90% – Pyrefly
> Let’s take a pause here for a second - the ‘CanIndex’ and ‘SupportsIndex’ from the looks are just “int”.
The PR for the change is https://github.com/numpy/numpy/pull/28913 - The details of files changed[0] shows the change was made in 'numpy/__init__.pyi'. Looking at the whole file[1] shows SupportsIndex is being imported from the standard library's typing module[2].
Where are you seeing SupportsIndex being defined as an int?
> I have a hard time dealing with these custom types because they are so obscure.
SupportsIndex is obscure, I agree, but it's not a custom type. It's defined in stdlib's typing module[2], and was added in Python 3.8.
[0]: https://github.com/numpy/numpy/pull/28913/files
[1]: https://github.com/charris/numpy/blob/c906f847f8ebfe0adec896...
[2]: https://docs.python.org/3/library/typing.html#typing.Support...
- Don’t Let Cyber Risk Kill Your GenAI Vibe: A Developer’s Guide
Know (or check) tells of older versions, such as the python sdk of OpenAI changing from a client with global state in v0.x.x, to a declared instance in v1.x.x, or numpy's change in how random generators are declared.
- Top 5 GitHub Repositories for Data Science in 2026
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A…
- Your 2025 Roadmap to Becoming an AI Engineer for Free for Vue.js Developers
AI starts with math and coding. You don’t need a PhD—just high school math like algebra and some geometry. Linear algebra (think matrices) and calculus (like slopes) help understand how AI models work. Python is the main language for AI, thanks to tools like TensorFlow and NumPy. If you know JavaScript from Vue.js, Python’s syntax is straightforward.
- Top 17 Tools for Scientific Simulation & Modeling
- Release v2.3.0 (June 7, 2025) · NumPy/NumPy
- How to Get Started with Scikit-Learn: A Beginner-Friendly Guide to Machine Learning in Python
As is the case with most Python libraries, it is open-source and free-to-use, making it easily accessible by anyone willing to learn machine learning, and it is built upon other open-source libraries within Python, like SciPy for advanced scientific operations, NumPy for efficient numerical computations, Matplotlib for data visualization, and Cython for increased efficiency and speed, similar to that of C/C++.
Keras
- PyTorch vs TensorFlow 2025: Which one wins after 72 hours?
Keras 3 multi-backend
- 🚗👁️ Segmentation d'Images pour pour le système embarqué d’une voiture autonome
- Top Programming Languages for AI Development in 2025
The unchallenged leader in AI development is still Python. and Keras, and robust community support.
- A Man Out to Prove How Dumb AI Still Is
>Chollet, a French computer scientist and one of the industry’s sharpest skeptics
I feel like this description really buries the lede on Chollet's expertise. (For those who don't know, he's the creator of and lead contributor[0] to Keras)
[0]https://github.com/keras-team/keras/graphs/contributors
- Building a Sarcasm Detection System with LSTM and GloVe: A Complete Guide
Keras API reference
- Submitting GPU jobs to Slurm @ Loyola University Chicago
- Top 8 OpenSource Tools for AI Startups
Star on GitHub ⭐ - Keras
- Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck.
- Las 10 Mejores Herramientas de Inteligencia Artificial de Código Abierto
(https://dev-to-uploads.s3.amazonaws.com/uploads/articles/92cup4lywcjfq83xg0ea.png)
- Using Google Magika to build an AI-powered file type detector
The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models.
What are some alternatives?
mitmproxy - An interactive TLS-capable intercepting HTTP proxy for penetration testers and software developers.
scikit-learn - scikit-learn: machine learning in Python
SymPy - A computer algebra system written in pure Python
tensorflow - An Open Source Machine Learning Framework for Everyone
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
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.