Based on our record, Numba seems to be a lot more popular than Apple Core ML. While we know about 92 links to Numba, we've tracked only 7 mentions of Apple Core ML. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
On the machine learning side of AI, they have CoreML. You can drag-and-drop images into Xcode to train an image classifier. And run the models on device, so if solar flares destroy the cell phone network and terrorists bomb all the data centers, your phone could still tell you if it's a hot dog or not. https://developer.apple.com/machine-learning/ https://developer.apple.com/machine-learning/core-ml/... - Source: Hacker News / 2 months ago
Apple has actually created ML chipsets, so AI can be executed natively, on-device. https://developer.apple.com/machine-learning/. - Source: Hacker News / 4 months ago
For your reference, Apple's pages for Machine Learning for Developers and for their research. The Apple Neural Engine was custom designed to work better with their proprietary machine learning programs -- and they've been opening up access to developers by extending support / compatibility for TensorFlow and PyTorch. They've also got CoreML, CreateML, and various APIs they are making to allow more use of their... Source: 12 months ago
> It’d be one thing if Apple actually worked on AI softwares a bit and made it readily available to developers. * Apple Silicon CPUs have a Neural Engine specifically made for fast ML-inference * Apple supports PyTorch (https://developer.apple.com/metal/pytorch/) * Apple has its own easily accessible machine-learning framework called Core-ML (https://developer.apple.com/machine-learning/) So it would be inaccurate... - Source: Hacker News / 12 months ago
This is the developer documentation where they advertise the APIs - https://developer.apple.com/machine-learning/. Source: over 2 years ago
I believe it is using Numba which converts to machine code. https://numba.pydata.org/. - Source: Hacker News / about 2 months ago
Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code. - Source: dev.to / 7 months ago
Or you use numba [1]. Then you can use a subset of plain Python. [1] https://numba.pydata.org/. - Source: Hacker News / 8 months ago
Simulations are, at least in my experience, numba’s [0] wheelhouse. [0]: https://numba.pydata.org/. - Source: Hacker News / 9 months ago
That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees. Source: 10 months ago
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