Capability
Automatic Differentiation And Gradient Computation
11 artifacts provide this capability.
Want a personalized recommendation?
Find the best match →Top Matches
1
Keras 3Framework44/100
Multi-backend deep learning API for JAX, TF, and PyTorch.
Unique: Keras 3's autodiff integration is transparent and backend-agnostic: the same model code automatically uses JAX's `grad`, TensorFlow's `GradientTape`, or PyTorch's `autograd` depending on the compiled backend, with no explicit gradient computation calls required in user code.
vs others: Simpler than PyTorch's explicit `loss.backward()` calls, and more flexible than TensorFlow's `tf.function` which requires graph-mode compilation; Keras 3 supports both eager and graph execution transparently.