Prerequisites: Solid understanding of Python, core machine learning principles, and fundamental TensorFlow/Keras usage (model building, training). Familiarity with calculus and linear algebra recommended.

Level: Advanced

What You'll Learn

• Performance Optimization

  Optimize TensorFlow models for speed and efficiency on GPUs/TPUs using profiling, mixed precision, and XLA.

• Distributed Training

  Implement various distributed training strategies (data/model parallelism) using tf.distribute.Strategy for large datasets and models.

• Custom Components

  Build custom layers, models, loss functions, metrics, and training loops using TensorFlow's advanced APIs.

• Production Pipelines

  Construct production-ready machine learning pipelines using TensorFlow Extended (TFX) components and orchestration.

• Model Deployment

  Deploy TensorFlow models for scalable inference using TensorFlow Serving and optimize models for edge devices with TensorFlow Lite.

• Advanced Architectures

  Implement components of sophisticated model architectures like Transformers and Generative Adversarial Networks (GANs).