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

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models. Topics Spotlight: AI-ready data centers ...

The short explanation for this is that Keras is a bit simplistic and too slow for the demands that most deep learning practitioners have. PyTorch is still growing, while TensorFlow’s growth has ...

TensorFlow is an open source machine learning framework developed by Google, designed to build and train AI models for a wide range of applications. The tool is widely used in industries such as ...

There are many open-source machine learning libraries for Python, including TensorFlow, PyTorch, Scikit-learn, Keras, and Theano. These libraries are free to use and have a large community of ...

This article will discuss the seven popular tools and frameworks used for developing AI applications: TensorFlow, PyTorch, Keras, Caffe, Microsoft Cognitive Toolkit, Theano and Apache MXNet.

Developers can submit ML training jobs created in TensorFlow, Keras, PyTorch, Scikit-learn, and XGBoost. Google now offers in-built algorithms based on linear classifier, wide and deep and XGBoost ...

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.

“Most strong deep learning teams today use one of the more popular frameworks – and I’m talking about technologies like Tensorflow, Keras, PyTorch, MXNet or Caffe.