In the realm of artificial intelligence and machine learning, Keras serves as a high-level API for building and training deep learning models. It simplifies the process of creating neural networks through its user-friendly interface, allowing developers to focus on model architecture rather than intricate coding details. Keras supports various model types, including Sequential and Functional APIs, and provides built-in methods for training, evaluation, and prediction. This flexibility enables users to customize their training loops and create new layers, making Keras a powerful tool for both beginners and experienced practitioners in the field of data science and AI.
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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Learn basic and advanced concepts of TensorFlow such as eager execution, Keras high-level APIs and flexible model building.
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TF-NumPy Type Promotion
There are 4 options for type promotion in TensorFlow. By default, TensorFlow raises errors instead of promoting types for mixed type operations. Running tf.numpy.experimental_enable_numpy_behavior() s...
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Customizing Saving and Serialization
A more advanced guide on customizing saving for your layers and models.
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Keras: The high-level API for TensorFlow
Introduction to Keras, the high-level API for TensorFlow.
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The Functional API
Complete guide to the functional API.
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Training & evaluation with the built-in methods
Complete guide to training & evaluation with `fit()` and `evaluate()`.
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Serialization and saving
Complete guide to saving & serializing models.
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Making new layers and models via subclassing
Complete guide to writing `Layer` and `Model` objects from scratch.
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Understanding masking & padding
Complete guide to using mask-aware sequence layers in Keras.
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Working with RNNs
Complete guide to using & customizing RNN layers.
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Writing your own callbacks
Complete guide to writing new Keras callbacks.
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