## Basic Neural Networks

The first few Tensorflow tutorials guide you through training and testing a simple neural network to classify handwritten digits from the MNIST database of digit images.

### MNIST For ML Beginners

If you're new to machine learning, we recommend starting here. You'll learn about a classic problem, handwritten digit classification (MNIST), and get a gentle introduction to multiclass classification.

### Deep MNIST for Experts

If you're already familiar with other deep learning software packages, and are already familiar with MNIST, this tutorial will give you a very brief primer on TensorFlow.

### TensorFlow Mechanics 101

This is a technical tutorial, where we walk you through the details of using TensorFlow infrastructure to train models at scale. We use MNIST as the example.

## Easy ML with tf.contrib.learn

### tf.contrib.learn Quickstart

A quick introduction to tf.contrib.learn, a high-level API for TensorFlow. Build, train, and evaluate a neural network with just a few lines of code.

### Overview of Linear Models with tf.contrib.learn

An overview of tf.contrib.learn's rich set of tools for working with linear models in TensorFlow.

### Linear Model Tutorial

This tutorial walks you through the code for building a linear model using tf.contrib.learn.

### Wide and Deep Learning Tutorial

This tutorial shows you how to use tf.contrib.learn to jointly train a linear model and a deep neural net to harness the advantages of each type of model.

### Logging and Monitoring Basics with tf.contrib.learn

This tutorial shows you how to use TensorFlowâ€™s logging capabilities and the Monitor API to audit the in-progress training of a neural network.

### Building Input Functions with tf.contrib.learn

This tutorial introduces you to creating input functions in tf.contrib.learn,
and walks you through implementing an `input_fn`

to train a neural network
for predicting median house values.

## TensorFlow Serving

### TensorFlow Serving

An introduction to TensorFlow Serving, a flexible, high-performance system for serving machine learning models, designed for production environments.

## Image Processing

### Convolutional Neural Networks

An introduction to convolutional neural networks using the CIFAR-10 data set. Convolutional neural nets are particularly tailored to images, since they exploit translation invariance to yield more compact and effective representations of visual content.

### Image Recognition

How to run object recognition using a convolutional neural network trained on ImageNet Challenge data and label set.

### Deep Dream Visual Hallucinations

Building on the Inception recognition model, we will release a TensorFlow version of the Deep Dream neural network visual hallucination software.

## Language and Sequence Processing

### Vector Representations of Words

This tutorial motivates why it is useful to learn to represent words as vectors
(called *word embeddings*). It introduces the word2vec model as an efficient
method for learning embeddings. It also covers the high-level details behind
noise-contrastive training methods (the biggest recent advance in training
embeddings).

### Recurrent Neural Networks

An introduction to RNNs, wherein we train an LSTM network to predict the next word in an English sentence. (A task sometimes called language modeling.)

### Sequence-to-Sequence Models

A follow on to the RNN tutorial, where we assemble a sequence-to-sequence model for machine translation. You will learn to build your own English-to-French translator, entirely machine learned, end-to-end.

### SyntaxNet: Neural Models of Syntax

An introduction to SyntaxNet, a Natural Language Processing framework for TensorFlow.

## Non-ML Applications

### Mandelbrot Set

TensorFlow can be used for computation that has nothing to do with machine learning. Here's a naive implementation of Mandelbrot set visualization.

### Partial Differential Equations

As another example of non-machine learning computation, we offer an example of a naive PDE simulation of raindrops landing on a pond.