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- Keras: Deep Learning for humans
Keras is a deep learning API designed for human beings, not machines Keras focuses on debugging speed, code elegance conciseness, maintainability, and deployability
- Getting started with Keras
Getting started with Keras Learning resources Are you a machine learning engineer looking for a Keras introduction one-pager? Read our guide Introduction to Keras for engineers Want to learn more about Keras 3 and its capabilities? See the Keras 3 launch announcement Are you looking for detailed guides covering in-depth usage of different parts of the Keras API? Read our Keras developer
- Keras: Deep Learning for humans
Keras 3 is a full rewrite of Keras that enables you to run your Keras workflows on top of either JAX, TensorFlow, PyTorch, or OpenVINO (for inference-only), and that unlocks brand new large-scale model training and deployment capabilities You can pick the framework that suits you best, and switch from one to another based on your current goals
- Developer guides - Keras
Keras documentation: Developer guides Developer guides Our developer guides are deep-dives into specific topics such as layer subclassing, fine-tuning, or model saving They're one of the best ways to become a Keras expert Most of our guides are written as Jupyter notebooks and can be run in one click in Google Colab, a hosted notebook environment that requires no setup and runs in the cloud
- Code examples - Keras
Keras documentation: Code examples Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows
- Keras 3 API documentation
Keras Applications Xception EfficientNet B0 to B7 EfficientNetV2 B0 to B3 and S, M, L ConvNeXt Tiny, Small, Base, Large, XLarge VGG16 and VGG19 ResNet and ResNetV2 MobileNet, MobileNetV2, and MobileNetV3 DenseNet NasNetLarge and NasNetMobile InceptionV3 InceptionResNetV2
- About Keras 3
About Keras 3 Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch Keras is: Simple – but not simplistic Keras reduces developer cognitive load to free you to focus on the parts of the problem that really matter Flexible – Keras adopts the principle of progressive disclosure of complexity: simple workflows should be quick and
- Keras Applications
Keras documentation: Keras Applications Keras Applications Keras Applications are deep learning models that are made available alongside pre-trained weights These models can be used for prediction, feature extraction, and fine-tuning Weights are downloaded automatically when instantiating a model They are stored at ~ keras models Upon instantiation, the models will be built according to
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