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We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine.
Create machine learning models in TensorFlow
Use the TensorFlow libraries to solve numerical problems
Troubleshoot and debug common TensorFlow code pitfalls
Use tf.estimator to create, train, and evaluate an ML model
Train, deploy, and productionalize ML models at scale with Cloud ML Engine