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 easy to use, making it accessible to beginners and experienced users alike.
  • High-Level API: Keras provides a high-level API that simplifies the process of building and training neural networks.
  • Extensible: Keras can be extended with custom layers, models, and training callbacks.
  • Comprehensive Documentation: Keras has extensive documentation that covers all aspects of the library.

Getting Started

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

pip install keras

After installation, you can import Keras in your Python script:

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

Quick Example

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

model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(100,)))
model.add(Dense(1, activation='sigmoid'))

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

Resources

For more information on Keras, please refer to the following resources:

Additional Reading

Here's an image of a neural network to inspire you:

neural_network