Deep Learning Frameworks

Deep learning frameworks are essential tools for implementing and experimenting with neural networks. Here are some of the most popular frameworks in the field:

TensorFlow 🧠: An open-source library developed by Google Brain, TensorFlow is highly scalable and flexible.
PyTorch 🐬: Developed by Facebook AI Research, PyTorch is known for its dynamic computation graph and ease of use.
Keras 🧠: Keras is an open-source neural network library written in Python. It is designed to be user-friendly and modular.
Caffe 📚: Caffe is a deep learning framework made with expression, speed, and modularity in mind.

For more information on deep learning frameworks, check out our Deep Learning Tutorial.

Here's an example of how you can use TensorFlow to create a simple neural network:

import tensorflow as tf

# Create a basic model
model = tf.keras.models.Sequential([
  tf.keras.layers.Dense(10, activation='relu', input_shape=(100,)),
  tf.keras.layers.Dense(1, activation='sigmoid')
])

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

# Train the model
model.fit(x_train, y_train, epochs=10)