TensorFlow 1.x & 2.0

KFU course for Master

Syllabus

1. Introduction (Lesson1)

TensorFlow 1.x module

2. TensorFlow Basics (Lesson1, Appendix_Lesson1-2)
3. TensorFlow program for Euclid distance calculation (Lesson2)
4. Control Dependencies (Lesson2, Appendix_Lesson1-2, Appendix_Lesson2)
5. Debug in TensorFlow 1.x (Lesson2)
6. Data Input Pipeline: TensorFlow Queue (Lesson3)
7. Data Input Pipeline:TensorFlow Queue: multithreading (Lesson4)
8. Data Input Pipeline: tf.data API and Performance (Lesson5)
9. Image Classification: TF Queue and tf.data (Lesson6)
10. High Level API: Keras & Estimator (Lesson7)
11. TensorFlow 1.x: Tricks, Pitfalls and Workarounds (Lesson8, Appendix_Lesson8)

TensorFlow 2.0 module

12. TensorFlow 2.0 Introduction: Comparison with TensorFlow 1.x, AutoGraph, Build Model, Custom Layers/Model/Loss/Accuracy/Callbacks, tf.GradientTape (Lesson9)
13. Image Classification: Custom model, custom accuracy (Lesson10, Lesson11)
14. Logging and Visualization: TensorBoard, TensorBoard.dev, MLflow and Tensorspacejs (Lesson12)
15. C++ Op and Tricks, Pitfalls and Workarounds of TF 2.0 (Lesson13)