TensorFlow Guide
The “TensorFlow Guide” delves into the intricacies of utilizing TensorFlow for machine learning and artificial intelligence applications. It explores topics such as data augmentation, deterministic bridge engineering, and the challenges of enterprise RAG implementations. The guide emphasizes the importance of understanding and leveraging tools like Spark, EMR on EKS, and Airflow 3 to build secure and responsive AI systems. Additionally, it highlights the significance of context, precision, and multi-tenancy in developing effective machine learning models. Overall, the guide provides insights into advanced techniques and technologies for optimizing TensorFlow-based projects.
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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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