Keras Documentation

Keras is an open-source software library that provides a Python interface for defining, training, and evaluating deep learning models. It is designed to enable fast experimentation with deep neural networks.

Features

User-Friendly: Keras is designed to be user-friendly and easy to use, making it accessible to both beginners and experienced deep learning practitioners.
High-Level API: Keras provides a high-level API that allows users to quickly and easily define and run deep learning models.
Extensive Preprocessing Layers: Keras includes a wide range of preprocessing layers for data normalization, augmentation, and more.
Integration with TensorFlow, Theano, and CNTK: Keras can be used with TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK), providing flexibility and compatibility with different backends.

Quick Start

To get started with Keras, you can install it using pip:

pip install keras

Once installed, you can import Keras and create a simple neural network:

from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(64, activation='relu', input_shape=(32,)))
model.add(Dense(10, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

Resources

For more information and resources on Keras, please visit the official Keras documentation.