Introduction | To Neural Networks Using Matlab 60 Sivanandam Pdf Extra Quality

% Prepare data X = rand(1000,2); Y = categorical(double(sum(X,2)>1)); ds = arrayDatastore(X,'IterationDimension',1); cds = combine(ds, arrayDatastore(Y)); trainedNet = trainNetwork(cds, layers, options); 4.4 Implementing backprop from scratch (single hidden layer)

% Example using a simple feedforward net with fullyConnectedLayer layers = [ featureInputLayer(2) fullyConnectedLayer(10) reluLayer fullyConnectedLayer(2) softmaxLayer classificationLayer]; % Prepare data X = rand(1000,2); Y =

4.1 Single-layer perceptron (from-scratch) % Prepare data X = rand(1000