Keras and TensorFlow are both deep learning frameworks that are widely used in the machine learning community. They have a lot of overlapping functionality but they also have some differences.
TensorFlow is an open-source deep learning framework developed by Google. It provides a low-level API for building and training machine learning models. This means that TensorFlow gives users a lot of control over the model architecture and optimization process. TensorFlow has a lot of advanced features such as distributed computing, which can be used to scale up the training process to multiple GPUs, and automatic differentiation, which can be used to calculate gradients automatically.
Keras, on the other hand, is a high-level API developed by Francois Chollet. It is built on top of TensorFlow, Theano, or CNTK. Keras is designed to be user-friendly and easy to use, which makes it a popular choice for beginners in machine learning. Keras provides a simplified interface for building and training deep learning models. This means that users do not need to have a deep understanding of the low-level details of the model architecture or the optimization process.
One of the major differences between Keras and TensorFlow is the level of abstraction they provide. TensorFlow provides a lower-level API that gives users more control over the model architecture and optimization process, but this also means that it can be more challenging to use. Keras, on the other hand, provides a higher-level API that is easier to use but does not offer as much flexibility.
Another difference between Keras and TensorFlow is the number of pre-built models and layers available. TensorFlow has a larger collection of pre-built models and layers, while Keras provides a smaller, but still significant, set of these.
In summary, TensorFlow is a low-level API designed for users who have a deep understanding of deep learning models, architecture, and optimization. Keras is a higher-level API designed for users who want to quickly build and train deep learning models without a lot of knowledge of the underlying details.