Start using TensorFlow
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)
Solve real problems with machine learning
What are the new changes of TensorFlow
Explore ecosystems
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Library TensorFlow Lite Deploy ML on mobile and edge devices such as Android, iOS, Raspberry Pi, and Edge TPU. -
Library TensorFlow.js Train and run models directly in the browser using JavaScript or Node.js. -
API tf.data Preprocess data and create input pipelines for ML models. -
Library TFX Create production ML pipelines and implement MLOps best practices. -
API tf.keras Create ML models with TensorFlow's high-level API. -
Resource Kaggle Models Find pre-trained models ready for fine-tuning and deployment. -
Resource TensorFlow Datasets Browse the collection of standard datasets for initial training and validation. -
Tool TensorBoard Visualize and track development of ML models.
