TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is widely used for machine learning and deep learning applications.
Quick Overview
- Machine Learning: TensorFlow provides tools for building and deploying machine learning models.
- Deep Learning: It supports neural networks and other deep learning architectures.
- Dataflow: TensorFlow allows you to build and execute a directed dataflow graph.
Installation
To get started with TensorFlow, you need to install it. You can find the installation instructions on the TensorFlow website.
Getting Started
Here's a simple example of a TensorFlow program:
import tensorflow as tf
# Create a simple model
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation='relu', input_shape=(32,)),
tf.keras.layers.Dense(1)
])
# Compile the model
model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['mae', 'mse'])
# Generate some data
import numpy as np
x_train = np.random.random((1000, 32))
y_train = np.random.random((1000, 1))
# Train the model
model.fit(x_train, y_train, epochs=10)
Resources
