Keras

Keras is an open-source deep learning framework written in Python. It provides a high-level interface for building and training neural networks, making it easier and faster to create machine learning models. Keras is known for its simplicity and user-friendly design, and it can run on top of other deep learning libraries such as "TensorFlow" and "Theano".

-Key Features of Keras:
1- User-Friendly API:
  - Keras offers a simple, consistent API that makes it easy to build, train, and evaluate deep learning models with just a few lines of code.

2 - Modular and Extensible:
  - Keras is built with modularity in mind, meaning that it is easy to add new components, like layers, optimizers, or loss functions, to your models.

3.- Pre-built Layers and Models:
  - Keras provides a wide range of pre-built layers (e.g., convolutional, recurrent, dense) and pre-trained models (e.g., VGG16, ResNet), which can help developers quickly build complex architectures.

4 - Seamless Integration with TensorFlow:
  - While Keras can run on different backends, it is most commonly used with **TensorFlow**, where it acts as a high-level API to simplify the model-building process.

5.- Supports CPU and GPU:
  - Keras supports both CPU and GPU computation, allowing models to be trained faster on large datasets.

6.- Easy Model Evaluation and Tuning:
  - Keras provides built-in tools for evaluating models, performing cross-validation, and tuning hyperparameters.

Why Use Keras?
-  Simplicity: Keras is designed to be simple and easy to use, even for beginners in machine learning and deep learning.
-  Rapid Prototyping: Keras allows quick experimentation, enabling you to build and test models faster.
-  Wide Adoption: Keras is widely used in both research and industry, with a large community and plenty of tutorials and resources available.