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  • Keras: Deep Learning for humans
    KERAS 3 0 RELEASED A superpower for ML developers Keras is a deep learning API designed for human beings, not machines Keras focuses on debugging speed, code elegance conciseness, maintainability, and deployability When you choose Keras, your codebase is smaller, more readable, easier to iterate on
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    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 guides Are you looking for tutorials showing Keras in action across a wide range of use cases? See the
  • 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 Google Colab includes GPU and TPU runtimes Available guides The Functional API The Sequential
  • 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 easy, while arbitrarily advanced workflows should be possible via a clear path that builds upon
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    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
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    Keras documentation: Code examples Our code examples are short (less than 300 lines of code), focused demonstrations of vertical deep learning workflows
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    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 the image data format set in your Keras configuration file at ~ keras keras json For instance, if
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    Keras documentation: The Model class Once the model is created, you can config the model with losses and metrics with model compile(), train the model with model fit(), or use the model to do prediction with model predict() With the Sequential class In addition, keras Sequential is a special case of model where the model is purely a stack of single-input, single-output layers
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    Keras documentation: Natural Language Processing English-to-Spanish translation with a sequence-to-sequence Transformer





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