Using Keras, you can build a neural network model quickly and easily using minimal code, allowing for rapid prototyping. Keras, on the other hand, is perfect for those that do not have a strong background in Deep Learning, but still want to work with neural networks. However, it does have a steep learning curve. TensorFlow provides a comprehensive machine learning platform that offers both high level and low level capabilities for building and deploying machine learning models. Keras simplifies the implementation of complex neural networks with its easy to use framework.įigure 1: TensorFlow vs Keras When to Use Keras vs TensorFlow Keras, on the other hand, is a high-level API that runs on top of TensorFlow. TensorFlow is an open-source set of libraries for creating and working with neural networks, such as those used in Machine Learning (ML) and Deep Learning projects. What’s the Difference Between Tensorflow and Keras? Keras’ models offer a simple, user-friendly way to define a neural network, which will then be built for you by TensorFlow. Keras is a neural network Application Programming Interface (API) for Python that is tightly integrated with TensorFlow, which is used to build machine learning models.
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