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Source channel @githubtrending · Post #14659 · May 1

#cplusplus#arm#convolution#deep_learning#embedded_devices#llm#machine_learning#ml#mnn#transformer#vulkan#winograd_algorithm MNN is a lightweight and efficient deep learning framework that helps run AI models on mobile devices and other small devices. It supports many types of AI models and can handle tasks like image recognition and language processing quickly and locally on your device. This means you can use AI features without needing to send data to the cloud, which improves privacy and speed. MNN is used in many apps, including those from Alibaba, and supports various platforms like Android and iOS. It also helps reduce the size of AI models, making them faster and more efficient. https://github.com/alibaba/MNN

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djangoproject

@djangoproject · Post #542 · 12/28/2017, 12:38 PM

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http://contrib.scikit-learn.org/imbalanced-learn/ In an ideal world, we would have perfectly balanced datasets and we would all train models and be happy. Unfortunately, the real world is not like that, and certain tasks favor very imbalanced data. For example, when predicting fraud in credit card transactions, you would expect that the vast majority of the transactions (+99.9%?) are actually legit. Training #ML(#machine_learning) algorithms naively will lead to dismal performance, so extra care is needed when working with these types of datasets. #Imbalanced-learn is a Python package which offers implementations of some of those techniques, to make your life much easier.

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#ml #machine_learning #imbalanced