Dès la mi-2016, la plupart des grandes sociétés de tech avaient leur propre système de deep learning : MXNet, Chainer, Theano, TensorFlow, Keras, et un millier de bibliothèques plus réduites.

Scikit-learn, PyTorch, and TensorFlow remain core tools for structured data and deep learning tasks.New libraries like JAX, ...

Afin de simplifier la mise en place de projets de deep learning avec son outil open source TensorFlow, Google vient de lancer une librairie de workflow baptisée Tensor2Tensor (T2T).

Put another way, you write Keras code using Python. The Keras code calls into the TensorFlow library, which does all the work. In Keras terminology, TensorFlow is the called backend engine.

Even in TensorFlow 1.12, the official Get Started with TensorFlow tutorial uses the high-level Keras API embedded in TensorFlow, tf.keras.By contrast, the TensorFlow Core API requires working with ...

Keras proper, a high-level front end for building neural network models, ships with support for three back-end deep learning frameworks: TensorFlow, CNTK, and Theano. Amazon is currently working ...