What you'll learn

Production ML often requires training behavior that the standard Keras workflow cannot express cleanly. In this course, Advanced TensorFlow: Custom Training and Optimization, you’ll gain the ability to implement and debug custom training systems that scale. First, you’ll explore custom training loops with tf.GradientTape, including gradient transformations, learning rate schedules, mixed precision, and advanced regularization. Next, you’ll discover how to build custom layers, models, losses, metrics, optimizers, and custom train_step() implementations for specialized procedures. Finally, you’ll learn how to scale training with TensorFlow distribution strategies and apply these patterns to advanced architectures and workflows like GNNs, VAEs, GANs, reinforcement learning, and scikit-learn integration. When you’re finished with this course, you’ll have the skills and knowledge needed to build production-ready TensorFlow training pipelines with full control over optimization and execution.