# 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.

The following tutorial explains the interaction of CPUs and GPUs on a
TensorFlow system:

  * @{$using_gpu$Using GPUs}

The following tutorials cover different aspects of image recognition:

  * @{$image_recognition$Image Recognition}, which introduces the field of
    image recognition and a model (Inception) for recognizing images.
  * @{$image_retraining$How to Retrain Inception's Final Layer for New Categories},
    which has a wonderfully self-explanatory title.
  * @{$layers$A Guide to TF Layers: Building a Convolutional Neural Network},
    which introduces convolutional neural networks (CNNs) and demonstrates how
    to build a CNN in TensorFlow.
  * @{$deep_cnn$Convolutional Neural Networks}, which demonstrates how to
    build a small CNN for recognizing images.  This tutorial is aimed at
    advanced TensorFlow users.

The following tutorials focus on machine learning problems in human language:

  * @{$word2vec$Vector Representations of Words}, which demonstrates how to
    create an embedding for words.
  * @{$recurrent$Recurrent Neural Networks}, which demonstrates how to use a
    recurrent neural network to predict the next word in a sentence.
  * @{$seq2seq$Sequence-to-Sequence Models}, which demonstrates how to use a
    sequence-to-sequence model to translate text from English to French.

The following tutorials focus on linear models:

  * @{$linear$Large-Scale Linear Models with TensorFlow}, which introduces
    linear models and demonstrates how to build them with the high-level API.
  * @{$wide$TensorFlow Linear Model Tutorial}, which demonstrates how to solve
    a binary classification problem in TensorFlow.
  * @{$wide_and_deep$TensorFlow Wide & Deep Learning Tutorial}, which explains
    how to use the high-level API to jointly train both a wide linear model
    and a deep feed-forward neural network.
  * @{$kernel_methods$Improving Linear Models Using Explicit Kernel Methods},
    which shows how to improve the quality of a linear model by using explicit
    kernel mappings.
  * @{$audio_recognition$Simple Audio Recognition}, which shows how to
    build a basic speech recognition network.

The following tutorial covers building a classification model for sequences:

  * ${$recurrent_quickdraw$Classifying Drawings using Recurrent Neural Networks}

Although TensorFlow specializes in machine learning, you may also use
TensorFlow to solve other kinds of math problems.  For example:

  * @{$mandelbrot$Mandelbrot Set}
  * @{$pdes$Partial Differential Equations}
