--- /dev/null
+project_path: /_project.yaml
+book_path: /_book.yaml
+description: <!--no description-->
+landing_page:
+ show_side_navs: True
+ rows:
+ - description: >
+ <h1 class="hide-from-toc">Get Started with TensorFlow</h1>
+ <p>
+ TensorFlow is an open-source machine learning library for research and
+ production. TensorFlow offers APIs for beginners and experts to develop
+ for desktop, mobile, web, and cloud. See the sections below to get
+ started.
+ </p>
+ items:
+ - custom_html: >
+ <style>
+ .tfo-button-primary {
+ background-color: #fca851;
+ }
+ .tfo-button-primary:hover {
+ background-color: #ef6c02;
+ }
+
+ a.colab-button {
+ display: inline-block;
+ background: rgba(255, 255, 255, 0.75);
+ padding: 4px 8px;
+ border-radius: 4px;
+ font-size: 11px!important;
+ text-decoration: none;
+ color:#aaa;border: none;
+ font-weight: 300;
+ border: solid 1px rgba(0, 0, 0, 0.08);
+ border-bottom-color: rgba(0, 0, 0, 0.15);
+ text-transform: uppercase;
+ line-height: 16px
+ }
+ a.colab-button:hover {
+ color: #666;
+ background: white;
+ border-color: rgba(0, 0, 0, 0.2);
+ }
+ a.colab-button span {
+ background-image: url("/images/colab_logo_button.svg");
+ background-repeat:no-repeat;background-size:20px;
+ background-position-y:2px;display:inline-block;
+ padding-left:24px;border-radius:4px;
+ text-decoration:none;
+ }
+
+ /* adjust code block for smaller screens */
+ @media screen and (max-width: 1000px) {
+ .tfo-landing-row-item-code-block {
+ flex-direction: column !important;
+ }
+ .tfo-landing-row-item-code-block > .devsite-landing-row-item-code {
+ /*display: none;*/
+ width: 100%;
+ }
+ }
+ @media screen and (max-width: 720px) {
+ .tfo-landing-row-item-code-block {
+ display: none;
+ }
+ }
+ </style>
+ <div class="devsite-landing-row-item-description">
+ <a href="#">
+ <h3 class="hide-from-toc">Learn and use ML</h3>
+ </a>
+ <div class="devsite-landing-row-item-description-content">
+ <p>
+ The high-level Keras API provides building blocks to create and
+ train deep learning models. Start with these beginner-friendly
+ notebook examples, then read the
+ <a href="/programmers_guide/keras">TensorFlow Keras guide</a>.
+ </p>
+ <ol style="padding-left:20px;">
+ <li><a href="/get_started/basic_classification">Basic classification</a></li>
+ <li><a href="/get_started/basic_text_classification">Text classification</a></li>
+ <li><a href="/get_started/basic_regression">Regression</a></li>
+ <li><a href="/get_started/overfit_and_underfit">Overfitting and underfitting</a></li>
+ <li><a href="/get_started/save_and_restore_models">Save and load</a></li>
+ </ol>
+ </div>
+ <div class="devsite-landing-row-item-buttons" style="margin-top:0;">
+ <a class="button button-primary tfo-button-primary" href="/programmers_guide/keras">Read the Keras guide</a>
+ </div>
+ </div>
+ - classname: tfo-landing-row-item-code-block
+ code_block: |
+ <pre class="prettyprint">
+ 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(),
+ tf.keras.layers.Dense(512, activation=tf.nn.relu),
+ tf.keras.layers.Dropout(0.2),
+ tf.keras.layers.Dense(10, activation=tf.nn.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)
+ </pre>
+ {% dynamic if request.tld != 'cn' %}
+ <a class="colab-button" target="_blank" href="https://colab.sandbox.google.com/github/tensorflow/models/blob/master/samples/core/get_started/_index.ipynb">Run in a <span>Notebook</span></a>
+ {% dynamic endif %}
+
+ - items:
+ - custom_html: >
+ <div class="devsite-landing-row-item-description" style="border-right: 2px solid #eee;">
+ <a href="https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/notebooks">
+ <h3 class="hide-from-toc">Research and experimentation</h3>
+ </a>
+ <div class="devsite-landing-row-item-description-content">
+ <p>
+ Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with
+ these notebooks, then read the <a href="/programmers_guide/eager">eager execution guide</a>.
+ </p>
+ <ol style="padding-left:20px;">
+ <li>
+ {% dynamic if request.tld == 'cn' %}
+ <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb" class="external">Eager execution basics</a>
+ {% dynamic else %}
+ <a href="https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb" class="external">Eager execution basics</a>
+ {% dynamic endif %}
+ </li>
+ <li>
+ {% dynamic if request.tld == 'cn' %}
+ <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb" class="external">Automatic differentiation and gradient tapes</a>
+ {% dynamic else %}
+ <a href="https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb" class="external">Automatic differentiation and gradient tapes</a>
+ {% dynamic endif %}
+ </li>
+ <li>
+ {% dynamic if request.tld == 'cn' %}
+ <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb" class="external">Variables, models, and training</a>
+ {% dynamic else %}
+ <a href="https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb" class="external">Variables, models, and training</a>
+ {% dynamic endif %}
+ </li>
+ <li>
+ {% dynamic if request.tld == 'cn' %}
+ <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb" class="external">Custom layers</a>
+ {% dynamic else %}
+ <a href="https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb" class="external">Custom layers</a>
+ {% dynamic endif %}
+ </li>
+ <li><a href="/get_started/eager">Custom training walkthrough</a></li>
+ <li>
+ {% dynamic if request.tld == 'cn' %}
+ <a href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb" class="external">Example: Neural machine translation w/ attention</a>
+ {% dynamic else %}
+ <a href="https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb" class="external">Example: Neural machine translation w/ attention</a>
+ {% dynamic endif %}
+ </li>
+ </ol>
+ </div>
+ <div class="devsite-landing-row-item-buttons">
+ <a class="button button-primary tfo-button-primary" href="/programmers_guide/eager">Read the eager execution guide</a>
+ </div>
+ </div>
+ - custom_html: >
+ <div class="devsite-landing-row-item-description">
+ <a href="#">
+ <h3 class="hide-from-toc">ML at production scale</h3>
+ </a>
+ <div class="devsite-landing-row-item-description-content">
+ <p>
+ Estimators can train large models on multiple machines in a
+ production environment. Try the examples below and read the
+ <a href="/programmers_guide/estimators">Estimators guide</a>.
+ </p>
+ <ol style="padding-left: 20px;">
+ <li><a href="/tutorials/text_classification_with_tf_hub">How to build a simple text classifier with TF-Hub</a></li>
+ <li><a href="https://github.com/tensorflow/models/tree/master/official/boosted_trees">Classifying Higgs boson processes</a></li>
+ <li><a href="/tutorials/wide_and_deep">Wide and deep learning using estimators</a></li>
+ </ol>
+ </div>
+ <div class="devsite-landing-row-item-buttons">
+ <a class="button button-primary tfo-button-primary" href="/programmers_guide/estimators">Read the Estimators guide</a>
+ </div>
+ </div>
+
+ - description: >
+ <h2 class="hide-from-toc">Google Colab: An easy way to learn and use TensorFlow</h2>
+ <p>
+ <a href="https://colab.sandbox.google.com/notebooks/welcome.ipynb" class="external">Colaboratory</a>
+ is a Google research project created to help disseminate machine learning
+ education and research. It's a Jupyter notebook environment that requires
+ no setup to use and runs entirely in the cloud.
+ <a href="https://medium.com/tensorflow/colab-an-easy-way-to-learn-and-use-tensorflow-d74d1686e309" class="external">Read the blog post</a>.
+ </p>
+
+ - description: >
+ <h2 class="hide-from-toc">Build your first ML app</h2>
+ <p>Create and deploy TensorFlow models on web and mobile.</p>
+ background: grey
+ items:
+ - custom_html: >
+ <div class="devsite-landing-row-item-description" style="background: #fff; padding:32px;">
+ <a href="https://js.tensorflow.org">
+ <h3 class="hide-from-toc">Web developers</h3>
+ </a>
+ <div class="devsite-landing-row-item-description-content">
+ TensorFlow.js is a WebGL accelerated, JavaScript library to train and
+ deploy ML models in the browser and for Node.js.
+ </div>
+ </div>
+ - custom_html: >
+ <div class="devsite-landing-row-item-description" style="background: #fff; padding:32px;">
+ <a href="/mobile/tflite/">
+ <h3 class="hide-from-toc">Mobile developers</h3>
+ </a>
+ <div class="devsite-landing-row-item-description-content">
+ TensorFlow Lite is lightweight solution for mobile and embedded devices.
+ </div>
+ </div>
+
+ - description: >
+ <h2 class="hide-from-toc">Videos and updates</h2>
+ <p>
+ Subscribe to the TensorFlow
+ <a href="https://www.youtube.com/tensorflow" class="external">YouTube channel</a>
+ and <a href="https://blog.tensorflow.org" class="external">blog</a> for
+ the latest videos and updates.
+ </p>
+ items:
+ - description: >
+ <h3 class="hide-from-toc">Get started with TensorFlow's High-Level APIs</h3>
+ youtube_id: tjsHSIG8I08
+ buttons:
+ - label: Watch the video
+ path: https://www.youtube.com/watch?v=tjsHSIG8I08
+ - description: >
+ <h3 class="hide-from-toc">Eager execution</h3>
+ youtube_id: T8AW0fKP0Hs
+ background: grey
+ buttons:
+ - label: Watch the video
+ path: https://www.youtube.com/watch?v=T8AW0fKP0Hs
+ - description: >
+ <h3 class="hide-from-toc">tf.data: Fast, flexible, and easy-to-use input pipelines</h3>
+ youtube_id: uIcqeP7MFH0
+ buttons:
+ - label: Watch the video
+ path: https://www.youtube.com/watch?v=uIcqeP7MFH0
--- /dev/null
+# Next Steps
+
+## Learn more about TensorFlow
+
+* The [TensorFlow Guide](/programmers_guide) includes usage guides for the
+ high-level APIs, as well as advanced TensorFlow operations.
+* [Premade Estimators](/programmers_guide/premade_estimators) are designed to
+ get results out of the box. Use TensorFlow without building your own models.
+* [TensorFlow.js](https://js.tensorflow.org/) allows web developers to train and
+ deploy ML models in the browser and using Node.js.
+* [TFLite](/mobile/tflite) allows mobile developers to do inference efficiently
+ on mobile devices.
+* [TensorFlow Serving](/serving) is an open-source project that can put
+ TensorFlow models in production quickly.
+* The [ecosystem](/ecosystem) contains more projects, including
+ [Magenta](https://magenta.tensorflow.org/), [TFX](/tfx),
+ [Swift for TensorFlow](https://github.com/tensorflow/swift), and more.
+
+## Learn more about machine learning
+
+Recommended resources include:
+
+* [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/),
+ a course from Google that introduces machine learning concepts.
+* [CS 20: Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/),
+ notes from an intro course from Stanford.
+* [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/),
+ a course that teaches how convolutional networks work.
+* [Machine Learning Recipes](https://www.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal),
+ a video series that introduces basic machine learning concepts with few prerequisites.
+* [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python),
+ a book by Francois Chollet about the Keras API, as well as an excellent hands on intro to Deep Learning.
+* [Hands-on Machine Learning with Scikit-Learn and TensorFlow](https://github.com/ageron/handson-ml),
+ a book by Aurélien Geron's that is a clear getting-started guide to data science and deep learning.
+* [Deep Learning](https://www.deeplearningbook.org/), a book by Ian Goodfellow et al.
+ that provides a technical dive into learning machine learning.
projects / platform / upstream / tensorflow.git / commitdiff