Uma plataforma completa de machine learning

Introdução ao TensorFlow

TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples.

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(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='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)

Resolver problemas do mundo real com ML

Explore examples of how TensorFlow is used to advance research and build AI-powered applications.

Improving access to maternal health with on-device ML

Learn how TensorFlow Lite enables access to fetal ultrasound assessment, improving health outcomes for women and families around Kenya and the world.

Build recommendation systems with reinforcement learning

Learn how Spotify uses the TensorFlow ecosystem to design an extendable offline simulator and train RL Agents to generate playlists.

Implante modelos de linguagem grandes no Android

Saiba como otimizar e implantar LLMs com o TensorFlow Lite para aplicativos de IA generativa.

What's new in TensorFlow

Read the latest announcements from the TensorFlow team and community.

Join the community

Collaborate, find support, and share your projects by joining interest groups or attending developer events.

Aprenda ML

New to machine learning? Begin with TensorFlow's curated curriculums or browse the resource library of books, online courses, and videos.

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