Get Started with TensorFlow
TensorFlow is an open-source machine learning library for research and production. TensorFlow offers APIs for beginners and experts to develop for desktop, mobile, web, and cloud. See the sections below to get started.
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
Learn and use ML
The high-level Keras API provides building blocks to create and train deep learning models. Start with these beginner-friendly notebook examples, then read the TensorFlow Keras guide.
Research and experimentation
Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with these notebooks, then read the eager execution guide.
ML at production scale
Estimators can train large models on multiple machines in a production environment. TensorFlow provides a collection of pre-made Estimators to implement common ML algorithms. See the Estimators guide.
Google Colab: An easy way to learn and use TensorFlow
Colaboratory is a Google research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. Read the blog post.
Build your first ML app
Create and deploy TensorFlow models on web and mobile.
Web developers
Mobile developers
Videos and updates
Subscribe to the TensorFlow YouTube channel and blog for the latest videos and updates.