TensorFlow is an end-to-end open source platform for machine learning

TensorFlow makes it easy for beginners and experts to create machine learning models. See the sections below to get started.



https://www.tensorflow.org/tutorials

Tutorials show you how to use TensorFlow with complete, end-to-end examples



https://www.tensorflow.org/guide

Guides explain the concepts and components of TensorFlow.



#### For beginners

The best place to start is with the user-friendly Sequential API. You can create models by plugging together building blocks. Run the “Hello World” example below, then visit the [tutorials](https://www.tensorflow.org/tutorials) to learn more.

To learn ML, check out our [education page](https://www.tensorflow.org/resources/learn-ml). Begin with curated curriculums to improve your skills in foundational ML areas.

```
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)
```

#### For experts

```
class MyModel(tf.keras.Model):
  def __init__(self):
    super(MyModel, self).__init__()
    self.conv1 = Conv2D(32, 3, activation='relu')
    self.flatten = Flatten()
    self.d1 = Dense(128, activation='relu')
    self.d2 = Dense(10, activation='softmax')

  def call(self, x):
    x = self.conv1(x)
    x = self.flatten(x)
    x = self.d1(x)
    return self.d2(x)
model = MyModel()

with tf.GradientTape() as tape:
  logits = model(images)
  loss_value = loss(logits, labels)
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
```

### Learn about the relationship between TensorFlow and Keras

TensorFlow's high-level APIs are based on the Keras API standard for defining and training neural networks. Keras enables fast prototyping, state-of-the-art research, and production—all with user-friendly APIs.

## Solutions to common problems

Explore step-by-step tutorials to help you with your projects.

https://www.tensorflow.org/tutorials/keras/classification

https://www.tensorflow.org/tutorials/generative/dcgan

https://www.tensorflow.org/tutorials/text/nmt_with_attention

## News & announcements

Check out our [blog](https://blog.tensorflow.org/search?label=TensorFlow+Core&max-results=20) for additional updates, and subscribe to our monthly TensorFlow newsletter to get the latest announcements sent directly to your inbox.