# Built-In Training

After defining the network architecture, you can define training
parameters using the `trainingOptions`

function. You
can then train the network using the `trainnet`

function. Use the trained network to predict class
labels or numeric responses.

## Apps

Deep Network Designer | Design and visualize deep learning networks |

## Functions

## Topics

**Create Simple Deep Learning Neural Network for Classification**This example shows how to create and train a simple convolutional neural network for deep learning classification.

**Train Convolutional Neural Network for Regression**This example shows how to train a convolutional neural network to predict the angles of rotation of handwritten digits.

**Time Series Forecasting Using Deep Learning**This example shows how to forecast time series data using a long short-term memory (LSTM) network.

**Sequence Classification Using Deep Learning**This example shows how to classify sequence data using a long short-term memory (LSTM) network.

**Sequence-to-Sequence Classification Using Deep Learning**This example shows how to classify each time step of sequence data using a long short-term memory (LSTM) network.

**Sequence-to-Sequence Regression Using Deep Learning**This example shows how to predict the remaining useful life (RUL) of engines by using deep learning.

**Sequence-to-One Regression Using Deep Learning**This example shows how to predict the frequency of a waveform using a long short-term memory (LSTM) neural network.

**Create Custom Deep Learning Training Plot**This example shows how to create a custom training plot that updates at each iteration during training of deep learning neural networks using

`trainnet`

.*(Since R2023b)***Custom Stopping Criteria for Deep Learning Training**This example shows how to stop training of deep learning neural networks based on custom stopping criteria using

`trainnet`

.*(Since R2023b)***Speed Up Deep Neural Network Training**Learn how to accelerate deep neural network training.