Welcome to the tutorial on Deep Learning with Keras. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. This tutorial will guide you through the basics of deep learning using Keras.

Overview

  • Deep Learning: A subset of machine learning that has gained popularity due to its ability to process and learn from large amounts of data.
  • Keras: A user-friendly neural networks library that simplifies the process of building and training neural networks.

Installation

Before you start, make sure you have Keras installed. You can install it using pip:

pip install keras

Getting Started

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

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

# Create a model
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

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

# Fit the model
model.fit(X_train, y_train, epochs=150, batch_size=10)

Further Reading

For more in-depth information, check out the Keras documentation.

Resources

Conclusion

Deep Learning with Keras is a powerful tool for building neural networks. With this tutorial, you should now have a basic understanding of how to get started with Keras. Happy learning!

Deep_Learning