Keras is a popular open-source deep learning framework that makes it easy to build and train neural networks. It was developed by François Chollet and first released in 2015. Keras is written in Python and provides a simple and intuitive API for building and training deep learning models.
Keras supports multiple backend engines, including TensorFlow, Microsoft Cognitive Toolkit, Theano, and PlaidML. It allows you to define models using a high-level, user-friendly API that is easy to understand and use, even if you are not an expert in machine learning.
With Keras, you can build a variety of neural network architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and multi-layer perceptrons (MLPs). Keras also includes pre-trained models, such as VGG16, Inception, and ResNet, that can be used for image classification, object detection, and other computer vision tasks.
Keras provides a number of features that make it easy to train and optimize your models, including built-in support for various loss functions, optimizers, and regularization techniques. It also includes tools for visualizing your training progress, such as plotting the loss and accuracy over time.
Overall, Keras is a powerful and easy-to-use tool for building and training deep learning models. It is widely used in industry and academia for a variety of applications, from image classification to natural language processing.