Dive deep into TensorFlow's inner workings through a comprehensive series of technical sessions led by the framework's developers. Explore topics such as parameter server training, TF NumPy, ML infrastructure building, quantization-aware training, and the new TF Lite Converter. Learn about TF debugging techniques, TF-Agents, tf.data and tf.distribute integration, TF Filesystems, and the TF Model Optimization Toolkit. Gain insights into eager execution runtime, graph rewriting, control flow, input pipeline optimization, and TensorBoard usage. Discover the intricacies of Resources and Variants, function-based programming, tf.distribute.Strategy, tf.Keras, TensorFlow Lite, AutoGraph, and MLIR for TF developers. Subscribe to the TensorFlow channel for access to this invaluable 17-hour learning resource.
Overview
Syllabus
Inside TensorFlow: Parameter server training.
Inside TensorFlow: TF NumPy.
Inside TensorFlow: Building ML infra.
Inside TensorFlow: Quantization aware training.
Inside TensorFlow: New TF Lite Converter.
Inside TensorFlow: TF Debugging.
Inside TensorFlow: TF-Agents.
Inside TensorFlow: tf.data + tf.distribute.
Inside TensorFlow: TF Filesystems.
Inside TensorFlow: TF Model Optimization Toolkit (Quantization and Pruning).
Inside TensorFlow: Eager execution runtime.
Inside TensorFlow: Graph rewriting (Macros, not functions).
Inside TensorFlow: Control Flow.
Inside TensorFlow: tf.data - TF Input Pipeline.
Inside TensorFlow: Summaries and TensorBoard.
Inside TensorFlow: Resources and Variants.
Inside TensorFlow: Functions, not sessions.
Inside TensorFlow: tf.distribute.Strategy.
Inside TensorFlow: tf.Keras (Part 1).
Inside TensorFlow: TensorFlow Lite.
Inside TensorFlow: tf.Keras (part 2).
Inside TensorFlow: AutoGraph.
Inside TensorFlow: MLIR for TF developers.
Taught by
TensorFlow
