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Git - git-imap-send Documentation
git-imap-send - Send a collection of patches from stdin to an IMAP folder This command uploads a mailbox generated with git format-patch into an IMAP drafts folder. This allows patches to be sent as o...
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4. Metadata Routing
This guide demonstrates how metadata can be routed and passed between objects in scikit-learn. If you are developing a scikit-learn compatible estimator or meta-estimator, you can check our related......
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Thread Safety in SciPy
Thread Safety in SciPy SciPy supports use in a multithreaded context via the threading module in the standard library. Many SciPy operations release the GIL, as does NumPy (and a lot of SciPy function...
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Parallel execution support in SciPy
Parallel execution support in SciPy SciPy aims to provide functionality that is performant, i.e. has good execution speed. On modern computing hardware, CPUs often have many CPU cores - and hence user...
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Stable Diffusion 3 in KerasHub!
Overview Stable Diffusion 3 is a powerful, open-source latent diffusion model (LDM) designed to generate high-quality novel images based on text prompts. Released by Stability AI , it was pre-trained ...
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Stable Diffusion 3 in KerasHub!
Overview Stable Diffusion 3 is a powerful, open-source latent diffusion model (LDM) designed to generate high-quality novel images based on text prompts. Released by Stability AI , it was pre-trained ...
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Semantic Segmentation with KerasHub
Background Semantic segmentation is a type of computer vision task that involves assigning a class label such as "person", "bike", or "background" to each individual pixel of an image, effectively div...
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Semantic Segmentation with KerasHub
Background Semantic segmentation is a type of computer vision task that involves assigning a class label such as "person", "bike", or "background" to each individual pixel of an image, effectively div...
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Getting Started with KerasHub
Installation and Setup To begin, let's install keras-hub. The library is available on PyPI, so we can simply install it with pip. Keras 3 was built to work on top of TensorFlow, Jax, and Torch backend...
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Classification with KerasHub
Inference with a pretrained classifier Let's get started with the simplest KerasHub API: a pretrained classifier. In this example, we will construct a classifier that was pretrained on the ImageNet da...
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Segment Anything in KerasHub!
Overview The Segment Anything Model (SAM) produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been t...
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LiteRT overview
Optimized for on-device machine learning : LiteRT addresses five key ODML constraints: latency (there's no round-trip to a server), privacy (no personal data leaves the device), connectivity (internet...
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