% Deep Learning Toolbox
% Version 24.1 (R2024a) 28-Dec-2023
%
% Training for Deep Learning
%   augmentedImageDatastore        - Generate batches of augmented image data
%   imageDataAugmenter             - Configure image data augmentation
%   dlnetwork                      - Deep learning neural network
%   trainnet                       - 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
%   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
%   instanceNormalizationLayer     - Instance normalization layer
%   layerNormalizationLayer        - Layer normalization 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
%   reluLayer                      - Rectified linear unit (ReLU) layer
%   sequenceInputLayer             - Sequence input layer
%   sigmoidLayer                   - Sigmoid layer
%   softmaxLayer                   - Softmax layer
%   swishLayer                     - Swish 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
%   deepNetworkQuantizer           - Quantize deep learning networks for deployment
%
% Extract and Visualize Features, Predict Outcomes for Deep Learning
%   deepDreamImage                 - Visualize network features using Deep Dream
%   occlusionSensitivity           - Explain network predictions by occluding the inputs
%   imageLIME                      - Explain network predictions using LIME
%   gradCAM                        - Explain network predictions using Grad-CAM
%   confusionmat                   - Confusion matrix for classification algorithms
%   confusionchart                 - Plot a confusion matrix
%
% Using Pretrained Networks for Deep Learning
%   imagePretrainedNetwork         - Pretrained neural network for image classification or regression
%   importONNXFunction             - Import neural network function from ONNX Format
%   importNetworkFromONNX          - Import pretrained networks from ONNX Format
%   exportONNXNetwork              - Export neural networks to ONNX Format
%   importNetworkFromTensorFlow    - Import pretrained networks from TensorFlow 
%   exportNetworkToTensorFlow      - Export neural networks to TensorFlow
%   importNetworkFromPyTorch       - Import pretrained networks from PyTorch
%
% Graphical User Interface Functions for Shallow Neural Networks
%   nnstart      - Neural Network Start GUI
%   nctool       - Neural Classification app
%   nftool       - Neural Fitting app
%   nntraintool  - Neural Network Training Tool
%   nprtool      - Neural Pattern Recognition app
%   ntstool      - Neural Time Series app
%   view         - View a neural network.
%
% Shallow Neural Network Creation Functions
%   cascadeforwardnet - Cascade-forward neural network.
%   competlayer       - Competitive neural layer.
%   distdelaynet      - Distributed delay neural network.
%   elmannet          - Elman neural network.
%   feedforwardnet    - Feed-forward neural network.
%   fitnet            - Function fitting neural network.
%   layrecnet         - Layered recurrent neural network.
%   linearlayer       - Linear neural layer.
%   lvqnet            - Learning vector quantization (LVQ) neural network.
%   narnet            - Nonlinear auto-associative time-series network.
%   narxnet           - Nonlinear auto-associative time-series network with external input.
%   newgrnn           - Design a generalized regression neural network.
%   newhop            - Create a Hopfield recurrent network.
%   newlind           - Design a linear layer.
%   newpnn            - Design a probabilistic neural network.
%   newrb             - Design a radial basis network.
%   newrbe            - Design an exact radial basis network.
%   patternnet        - Pattern recognition neural network.
%   perceptron        - Perceptron.
%   selforgmap        - Self-organizing map.
%   timedelaynet      - Time-delay neural network.
%
% Using Shallow Neural Networks
%   network     - Create a custom neural network.
%   sim         - Simulate a neural network.
%   init        - Initialize a neural network.
%   adapt       - Allow a neural network to adapt.
%   train       - Train a neural network.
%   disp        - Display a neural network's properties.
%   display     - Display the name and properties of a neural network
%   adddelay    - Add a delay to a neural network's response.
%   closeloop   - Convert neural network open feedback to closed feedback loops.
%   formwb      - Form bias and weights into single vector.
%   getwb       - Get all network weight and bias values as a single vector.
%   noloop      - Remove neural network open and closed feedback loops.
%   openloop    - Convert neural network closed feedback to open feedback loops.
%   removedelay - Remove a delay to a neural network's response.
%   separatewb  - Separate biases and weights from a weight/bias vector.
%   setwb       - Set all network weight and bias values with a single vector.
%
% Simulink Support for Shallow Neural Networks
%   gensim     - Generate a Simulink block to simulate a neural network.
%   setsiminit - Set neural network Simulink block initial conditions
%   getsiminit - Get neural network Simulink block initial conditions
%   neural     - Neural network Simulink blockset.
%
% Training Functions for Shallow Neural Networks
%   trainb    - Batch training with weight & bias learning rules.
%   trainbfg  - BFGS quasi-Newton backpropagation.
%   trainbr   - Bayesian Regulation backpropagation.
%   trainbu   - Unsupervised batch training with weight & bias learning rules.
%   trainc    - Cyclical order weight/bias training.
%   traincgb  - Conjugate gradient backpropagation with Powell-Beale restarts.
%   traincgf  - Conjugate gradient backpropagation with Fletcher-Reeves updates.
%   traincgp  - Conjugate gradient backpropagation with Polak-Ribiere updates.
%   traingd   - Gradient descent backpropagation.
%   traingda  - Gradient descent with adaptive lr backpropagation.
%   traingdm  - Gradient descent with momentum.
%   traingdx  - Gradient descent w/momentum & adaptive lr backpropagation.
%   trainlm   - Levenberg-Marquardt backpropagation.
%   trainoss  - One step secant backpropagation.
%   trainr    - Random order weight/bias training.
%   trainrp   - RPROP backpropagation.
%   trainru   - Unsupervised random order weight/bias training.
%   trains    - Sequential order weight/bias training.
%   trainscg  - Scaled conjugate gradient backpropagation.
%
% Plotting Functions for Shallow Neural Networks
%   plotconfusion  - Plot classification confusion matrix.
%   ploterrcorr    - Plot autocorrelation of error time series.
%   ploterrhist    - Plot error histogram.
%   plotfit        - Plot function fit.
%   plotinerrcorr  - Plot input to error time series cross-correlation.
%   plotperform    - Plot network performance.
%   plotregression - Plot linear regression.
%   plotresponse   - Plot dynamic network time-series response.
%   plotroc        - Plot receiver operating characteristic.
%   plotsomhits    - Plot self-organizing map sample hits.
%   plotsomnc      - Plot Self-organizing map neighbor connections.
%   plotsomnd      - Plot Self-organizing map neighbor distances.
%   plotsomplanes  - Plot self-organizing map weight planes.
%   plotsompos     - Plot self-organizing map weight positions.
%   plotsomtop     - Plot self-organizing map topology.
%   plottrainstate - Plot training state values.
%   plotwb         - Plot Hinton diagrams of weight and bias values.
%
% List of other Shallow Neural Network Implementation Functions
%   nnadapt       - Adapt functions.
%   nnderivative  - Derivative functions.
%   nndistance    - Distance functions.
%   nndivision    - Division functions.
%   nninitlayer   - Initialize layer functions.
%   nninitnetwork - Initialize network functions.
%   nninitweight  - Initialize weight functions.
%   nnlearn       - Learning functions.
%   nnnetinput    - Net input functions.
%   nnperformance - Performance functions.
%   nnprocess     - Processing functions.
%   nnsearch      - Line search functions.
%   nntopology    - Topology functions.
%   nntransfer    - Transfer functions.
%   nnweight      - Weight functions.
%
% Obsolete Functions
%   nntool        - replaced by nnstart

% Copyright 1992-2020 The MathWorks, Inc.
