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TensorFlow
An end-to-end platform for machine learning
Get started with 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)
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Explore examples of how TensorFlow is used to advance research and build AI-powered applications.
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TensorFlow GNN
Analyze relational data using graph neural networks
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Discover production-tested tools to accelerate modeling, deployment, and other workflows.
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tf.data\
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Preprocess data and create input pipelines for ML models. -
Library\
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TFX\
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Create production ML pipelines and implement MLOps best practices. -
API\
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tf.keras\
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Create ML models with TensorFlow's high-level API. -
Resource\
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Kaggle Models\
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Find pre-trained models ready for fine-tuning and deployment. -
Tool\
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TensorBoard\
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Visualize and track development of ML models.
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