Tutorials

This section contains tutorials demonstrating how to do specific tasks in TensorFlow. If you are new to TensorFlow, we recommend reading the documents in the "Get Started" section before reading these tutorials.

Images

These tutorials cover different aspects of image recognition:

• MNIST, which introduces convolutional neural networks (CNNs) and demonstrates how to build a CNN in TensorFlow.
• Image Recognition, which introduces the field of image recognition and uses a pre-trained model (Inception) for recognizing images.
• How to Retrain Inception's Final Layer for New Categories, which has a wonderfully self-explanatory title.
• Convolutional Neural Networks, which demonstrates how to build a small CNN for recognizing images. This tutorial is aimed at advanced TensorFlow users.

Sequences

These tutorials focus on machine learning problems dealing with sequence data.

• Recurrent Neural Networks, which demonstrates how to use a recurrent neural network to predict the next word in a sentence.
• Neural Machine Translation (seq2seq) Tutorial, which demonstrates how to use a sequence-to-sequence model to translate text from English to French.
• Recurrent Neural Networks for Drawing Classification builds a classification model for drawings, directly from the sequence of pen strokes.
• Simple Audio Recognition, which shows how to build a basic speech recognition network.

Data representation

These tutorials demonstrate various data representations that can be used in TensorFlow.

• TensorFlow Linear Model Tutorial, uses feature columns to feed a variety of data types to linear model, to solve a classification problem.
• TensorFlow Wide & Deep Learning Tutorial, builds on the above linear model tutorial, adding a deep feed-forward neural network component and a DNN-compatible data representation.
• Vector Representations of Words, which demonstrates how to create an embedding for words.
• Improving Linear Models Using Explicit Kernel Methods, which shows how to improve the quality of a linear model by using explicit kernel mappings.

Non Machine Learning

Although TensorFlow specializes in machine learning, the core of TensorFlow is a powerful numeric computation system which you can also use to solve other kinds of math problems. For example:

• Mandelbrot Set
• Partial Differential Equations