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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
  • Model training APIs - Keras
    Keras documentation: Model training APIs Returns the loss value metrics values for the model in test mode Computation is done in batches (see the batch_size arg ) Arguments x: Input data It can be: A NumPy array (or array-like), or a list of arrays (in case the model has multiple inputs) A backend-native tensor, or a list of tensors (in case the model has multiple inputs) A dict mapping input names to the corresponding array tensors, if the model has named inputs A keras utils
  • 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 the image data format set in your Keras configuration file at ~ keras keras json For instance, if
  • Building powerful image classification models using very little . . . - Keras
    Sun 05 June 2016 By Francois Chollet In Tutorials Note: this post was originally written in June 2016 It is now very outdated Please see this guide to fine-tuning for an up-to-date alternative, or check out chapter 8 of my book "Deep Learning with Python (2nd edition)" In this tutorial, we will present a few simple yet effective methods that you can use to build a powerful image classifier, using only very few training examples --just a few hundred or thousand pictures from each class
  • The Keras Blog
    Keras is a Deep Learning library for Python, that is simple, modular, and extensible Archives Github Documentation Google Group Building a simple Keras + deep learning REST API Mon 29 January 2018 By Adrian Rosebrock In Tutorials This is a guest post by Adrian Rosebrock Adrian is the author of PyImageSearch com, a blog about computer vision and deep learning Adrian recently finished authoring Deep Learning for Computer Vision with Python, a new book on deep learning for computer vision
  • Embedding layer - Keras
    This layer is an extension of keras layers Embedding for language models This layer can be called "in reverse" with reverse=True, in which case the layer will linearly project from output_dim back to input_dim By default, the reverse projection will use the transpose of the embeddings weights to project to input_dim (weights are "tied")
  • Parameter-efficient fine-tuning of Gemma with LoRA and QLoRA - Keras
    In this example, we will fine-tune KerasHub's Gemma model on the next token prediction task using LoRA and QLoRA Note that this example runs on all backends supported by Keras TensorFlow is only used for data preprocessing





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