A Detailed Overview of TensorFlow Core APIs

This article explains TensorFlow Core—the low-level, composable APIs for developers who need fine-grained control: researchers, framework authors, and power users extending Keras. It outlines use cases (custom layers/losses/optimizers, bespoke training loops, distribution with DTensor, advanced metrics), shows how Core underpins tools like Keras, TF Model Optimization, and TF Graphics, and highlights non-ML scientific workloads (physics sims, ray tracing, constrained optimization). You’ll meet the core building blocks—tf.Tensor/Variable/TensorArray, primitive ops, tf.math/linalg/random, tf.function, tf.GradientTape, DTensor, and tf.saved_model—plus guidance: don’t re-implement high-level APIs just to copy Keras; combine them when it helps. For hands-on learning, jump to the Quickstart for TensorFlow Core and the Build with Core tutorials.