% Deep Learning Functions
%
% Training for Deep Learning
%   assembleNetwork                - Assemble a neural network from pretrained layers
%   augmentedImageDatastore        - Generate batches of augmented image data
%   imageDataAugmenter             - Configure image data augmentation
%   dlnetwork                      - Neural network for custom training
%   trainNetwork                   - Train a neural network
%   trainingOptions                - Options for training a neural network
%
% Layers for Deep Learning
%   additionLayer                  - Addition layer
%   averagePooling2dLayer          - 2-D average pooling layer
%   averagePooling3dLayer          - 3-D average pooling layer
%   batchNormalizationLayer        - Batch normalization layer
%   bilstmLayer                    - Bidirectional long short-term memory (biLSTM) layer
%   classificationLayer            - Classification output layer for a neural network
%   clippedReluLayer               - Clipped rectified linear unit (ReLU) layer
%   concatenationLayer             - Concatenation layer
%   convolution2dLayer             - 2-D convolution layer for Convolutional Neural Networks
%   convolution3dLayer             - 3-D convolution layer for Convolutional Neural Networks
%   crop2dLayer                    - 2-D crop layer 
%   crop3dLayer                    - 3-D crop layer 
%   crossChannelNormalizationLayer - Local response normalization along channels
%   depthConcatenationLayer        - Depth concatenation layer
%   dropoutLayer                   - Dropout layer
%   eluLayer                       - Exponential linear unit (ELU) layer
%   featureInputLayer              - Feature input layer
%   flattenLayer                   - Flatten layer
%   fullyConnectedLayer            - Fully connected layer
%   globalAveragePooling2dLayer    - 2-D global average pooling layer
%   globalAveragePooling3dLayer    - 3-D global average pooling layer
%   globalMaxPooling2dLayer        - 2-D global max pooling layer
%   globalMaxPooling3dLayer        - 3-D global max pooling layer
%   groupedConvolution2dLayer      - 2-D grouped convolution layer
%   groupNormalizationLayer        - Group normalization layer
%   gruLayer                       - Gated recurrent unit (GRU) layer
%   imageInputLayer                - Image input layer
%   image3dInputLayer              - 3-D image input layer
%   leakyReluLayer                 - Leaky rectified linear unit (ReLU) layer
%   lstmLayer                      - Long short-term memory (LSTM) layer
%   maxPooling2dLayer              - 2-D max pooling layer
%   maxPooling3dLayer              - 3-D max pooling layer
%   maxUnpooling2dLayer            - Max unpooling layer
%   multiplicationLayer            - Element-wise Multiplication layer
%   regressionLayer                - Regression output layer for a neural network
%   reluLayer                      - Rectified linear unit (ReLU) layer
%   sequenceFoldingLayer           - Sequence folding layer
%   sequenceInputLayer             - Sequence input layer
%   sequenceUnfoldingLayer         - Sequence unfolding layer
%   sigmoidLayer                   - Sigmoid layer
%   softmaxLayer                   - Softmax layer
%   tanhLayer                      - Hyperbolic tangent layer
%   transposedConv2dLayer          - 2-D transposed convolution layer
%   transposedConv3dLayer          - 3-D transposed convolution layer
%
% Custom Layers for Deep Learning
%   checkLayer                     - Check layer validity
%   findPlaceholderLayers          - Find placeholder layers in a layer graph or layer array
%
% Apps for Deep Learning
%   analyzeNetwork                 - Analyze deep learning networks
%   deepNetworkDesigner            - Design and edit deep learning networks
%   experimentManager              - Design and run experiments to train and compare deep learning networks
%
% Extract and Visualize Features, Predict Outcomes for Deep Learning
%   deepDreamImage                 - Visualize network features using Deep Dream
%   occlusionSensitivity           - Determine how input data affects output activations by occluding the inputs
%   imageLIME                      - Explain image classification predictions using LIME
%   layerGraph                     - Create a layer graph
%   confusionmat                   - Confusion matrix for classification algorithms
%   confusionchart                 - Plot a confusion matrix
%
% Using Pretrained Networks for Deep Learning
%   alexnet                        - Pretrained AlexNet convolutional neural network
%   darknet19                      - Pretrained DarkNet-19 convolutional neural network
%   darknet53                      - Pretrained DarkNet-53 convolutional neural network
%   densenet201                    - Pretrained DenseNet-201 convolutional neural network
%   googlenet                      - Pretrained GoogLeNet convolutional neural network
%   inceptionv3                    - Pretrained Inception-v3 convolutional neural network
%   inceptionresnetv2              - Pretrained Inception-ResNet-v2 convolutional neural network
%   mobilenetv2                    - Pretrained MobileNetV2 convolutional neural network
%   nasnetmobile                   - Pretrained NASNet-Mobile convolutional neural network
%   nasnetlarge                    - Pretrained NASNet-Large convolutional neural network
%   resnet18                       - Pretrained ResNet-18 convolutional neural network
%   resnet50                       - Pretrained ResNet-50 convolutional neural network
%   resnet101                      - Pretrained ResNet-101 convolutional neural network
%   squeezenet                     - Pretrained SqueezeNet convolutional neural network
%   shufflenet                     - Pretrained ShuffleNet convolutional neural network
%   vgg16                          - Pretrained VGG-16 convolutional neural network
%   vgg19                          - Pretrained VGG-19 convolutional neural network
%   xception                       - Pretrained Xception convolutional neural network
%   importCaffeLayers              - Import Convolutional Neural Network Layers from Caffe
%   importCaffeNetwork             - Import Convolutional Neural Network Models from Caffe
%   importKerasLayers              - Import Convolutional Neural Network Layers from Keras
%   importKerasNetwork             - Import Convolutional Neural Network Models from Keras
%   importONNXFunction             - Import Convolutional Neural Network Function from ONNX Format
%   importONNXLayers               - Import Convolutional Neural Network Layers from ONNX Format
%   importONNXNetwork              - Import Convolutional Neural Network Models from ONNX Format
%   exportONNXNetwork              - Export Convolutional Neural Network Models to ONNX Format

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