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Create new deep networks for classification and regression, including
series, DAG, and LSTM networks, import from Caffe, or define your own
layers

Create new deep networks for image classification and regression,
including series, directed acyclic graph (DAG), and long short-term memory
(LSTM) networks. To create and train a new network, you can use the built-in
layers, define your own layers, or import layers from Caffe models. After
defining the network layers, you must define the training parameters using
`trainingOptions`

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

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

You can train a convolutional neural network on either a CPU, a GPU, or
multiple GPUs and/or in parallel. Training on a GPU or in parallel requires
the Parallel
Computing Toolbox™. Using a GPU requires a CUDA^{®} enabled NVIDIA^{®} GPU with compute capability 3.0 or higher. Specify the
execution environment using the `trainingOptions`

function.

**Create Simple Deep Learning Network for Classification**

This example shows how to create and train a simple convolutional neural network for deep learning classification.

**Resume Training from a Checkpoint Network**

Learn how to save checkpoint networks while training a convolutional neural network and resume training from a previously saved network

**Train Convolutional Neural Network for Regression**

This example shows how to fit a regression model using convolutional neural networks to predict the angles of rotation of handwritten digits.

**Sequence Classification Using Deep Learning**

This example shows how to classify sequence data using a long short-term memory (LSTM) network.

**Create and Train DAG Network for Deep Learning**

This example shows how to create and train a directed acyclic graph (DAG) network for deep learning.

**Train Residual Network on CIFAR-10**

This example shows how to create a deep learning neural network with residual connections and train it on CIFAR-10 data.

**Define Custom Deep Learning Layers**

Learn how to define custom deep learning layers

**Define a Custom Deep Learning Layer with Learnable Parameters**

This example shows how to define a PReLU layer and use it in a convolutional neural network.

**Define a Custom Regression Output Layer**

This example shows how to define a custom regression output layer with mean absolute error (MAE) loss and use it in a convolutional neural network.

**Define a Custom Classification Output Layer**

This example shows how to define a custom classification output layer with sum of squares error (SSE) loss and use it in a convolutional neural network.

Learn how to check the validity of custom deep learning layers

Discover deep learning capabilities in MATLAB^{®} using
convolutional neural networks for classification and regression, including
pretrained networks and transfer learning, and training on GPUs, CPUs,
clusters, and clouds.

A list of built-in deep learning layers in Neural Network Toolbox™

**Specify Layers of Convolutional Neural Network**

Learn about the layers of a convolutional neural network (ConvNet), and the order they appear in a ConvNet

**Set Up Parameters and Train Convolutional Neural Network**

Learn how to set up training parameters for a convolutional neural network

**Long Short-Term Memory Networks**

Learn about long short-term memory (LSTM) networks

**Preprocess Images for Deep Learning**

Learn how to resize images for training, prediction and classification, and how to preprocess images using data augmentation and mini-batch datastores.

**Develop Custom Mini-Batch Datastore**

Create a fully customized mini-batch datastore that contains training and test data sets for network training, prediction, and classification.

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