In the realm of artificial intelligence and machine learning, understanding the training and evaluation of models is crucial for developing effective algorithms. This process involves using built-in methods to streamline the training workflow, which includes defining loss functions, optimizers, and metrics. By leveraging frameworks like Keras, developers can create models using various architectures, such as Sequential or Functional APIs. Additionally, customizing training loops and implementing techniques like transfer learning can enhance model performance. Mastering these concepts is essential for anyone looking to excel in AI and data science applications.
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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