Welcome to the Keras Documentation page. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow. It is designed to enable fast experimentation with deep neural networks.

Overview

Keras provides a user-friendly interface for building and training neural networks. It offers a variety of pre-built layers, optimizers, and metrics, making it easy to get started with deep learning.

Key Features

  • User-friendly: Keras is designed to be simple and intuitive, making it easy for beginners and experienced users alike to build and train neural networks.
  • High-level API: Keras provides a high-level API, which simplifies the process of building and training neural networks.
  • Extensible: Keras can be extended with custom layers, models, and loss functions.

Getting Started

Before you dive into building your first neural network, it's important to understand the basic concepts of Keras. Here's a quick overview:

  • Layers: The fundamental building blocks of neural networks. Keras provides a variety of pre-built layers, such as Dense, Convolutional, and Recurrent layers.
  • Models: A model is a collection of layers. Keras provides several model configurations, including Sequential, Functional, and Model.
  • Training: Training a model involves feeding data through the model and adjusting the model's weights based on the output.

Examples

Here's a simple example of a neural network using Keras:

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'])

For more examples and tutorials, please visit our Keras tutorials page.

Resources

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