Training Models in Flax and TensorFlow

Choosing the right deep learning framework can make or break your machine learning pipeline. This text-based course provides a clear side-by-side comparison of training neural networks in Flax and TensorFlow. You will transition from basic concepts to advanced execution, understanding when to leverage JAX-based functional programming versus TensorFlow's graph-based ecosystem. Learn how to design, train, and scale models efficiently across different hardware. What you'll learn: Understand the foundational architecture differences between Flax and TensorFlow; Build and compile training loops using both JAX-based state management and TensorFlow Keras; Implement distributed training strategies across multiple GPUs and Cloud TPUs; Evaluate model performance using framework-specific metrics and validation pipelines; Practice optimizing data pipelines for both ecosystems to prevent training bottlenecks. The course begins with core definitions and architectural comparisons, guiding you through step-by-step code implementations of training loops, evaluation steps, and hardware scaling. This course is designed for beginner to intermediate machine learning practitioners who have a basic grasp of Python and neural networks, with no prior experience in Flax required. Start reading to master modern deep learning workflows across both powerful ecosystems.