Which is better: TensorFlow, PyTorch or Keras?

None is universally better. TensorFlow and PyTorch are full frameworks, while Keras 3 is a high-level API that runs on TensorFlow, PyTorch or JAX. Choose using a representative model, deployment target, quality metrics, performance, team capability and lifecycle cost.

Is Keras a deep learning framework?

Keras 3 is best described as a multi-backend deep-learning API. It supplies model-building and training abstractions but relies on TensorFlow, PyTorch or JAX as its backend runtime.

Is TensorFlow a framework?

Yes. TensorFlow is an end-to-end machine-learning framework with tensor operations, automatic differentiation, training, distribution and a wider production ecosystem including TFX and serving targets.

Is PyTorch or Keras better for beginners building neural networks?

Keras often gets a standard model to a readable baseline faster. PyTorch can be better when the learner needs direct control over training loops or will join a PyTorch codebase. A beginner should still learn data splits, error analysis, reproducibility and deployment constraints.

Can Keras use PyTorch and TensorFlow?

Yes. Keras 3 supports TensorFlow, PyTorch and JAX backends. Portable models should prefer built-in Keras layers and keras.ops; backend-specific operations can make a component dependent on one backend.

Should scikit-learn be included in a deep learning framework comparison?

Include it as a classical or small-MLP baseline when appropriate, not as an equivalent deep-learning platform. Scikit-learn's maintainers explicitly place complex deep learning outside the library's scope.

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