Keras and Applications

- Overview

TensorFlow and Keras are both popular open-source machine learning frameworks. TensorFlow is a more general-purpose framework, while Keras is a high-level API on top of TensorFlow that makes it easier to build and train machine learning models. 

Keras is the high-level API of the TensorFlow platform. It provides an approachable, highly-productive interface for solving ML problems, with a focus on modern deep learning. Keras covers every step of the machine learning workflow, from data processing to hyperparameter tuning to deployment. It was developed with a focus on enabling fast experimentation.

Here are some of the advantages of using TensorFlow: 

• It is a very versatile framework that can be used for a wide variety of machine learning tasks.
• It has a large and active community that is constantly developing new tools and resources.
• It is well-documented and there are many tutorials and examples available online.

Here are some of the advantages of using Keras: 

• It is a very user-friendly framework that is easy to learn and use.
• It has a high-level API that makes it easy to build and train machine learning models.
• It is well-integrated with TensorFlow, so you can use all of the features of TensorFlow with Keras.

Which framework you choose will depend on your specific needs and preferences. If you are new to machine learning, Keras is a good choice because it is easy to learn and use. If you need a framework that is more flexible and powerful, TensorFlow is a good choice.

Please refer to the following for more information:

• Wikipedia: TensorFlow
• Introduction to TensorFlow
• Wikipedia: Keras
• Introduction to Keras