This course provides an inside look into the TensorFlow team's internal training sessions, offering technical deep dives into TensorFlow by the developers themselves. The course covers topics such as parameter server training, TF NumPy, building ML infrastructure, quantization aware training, TF Lite Converter, debugging, TF-Agents, and more. By the end of the course, learners will gain a deep understanding of TensorFlow's advanced features and tools, enabling them to optimize models, work with distributed training, utilize TensorBoard for visualization, and leverage TF Keras for machine learning tasks. The course is designed for intermediate to advanced learners interested in mastering TensorFlow's capabilities and enhancing their machine learning skills.
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
