TensorFlow

엔드 투 엔드 머신러닝 플랫폼

TensorFlow 시작하기

TensorFlow를 사용하면 어떤 환경에서도 실행할 수 있는 ML 모델을 쉽게 만들 수 있습니다. 대화형 코드 샘플을 통해 직관적인 API를 사용하는 방법을 알아보세요.

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)

TensorFlow GNN

Analyze relational data using graph neural networks

GNNs can process complex relationships between objects, making them a powerful technique for traffic forecasting, medical discovery, and more.

Learn about TF GNN

LiteRT

온디바이스 ML로 산모 건강에 대한 접근성 개선

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

Explore LiteRT