Open Source Deep Learning Project: ConvNet

Deep Learning Specialization on Coursera

ConvNet: Convolutional Neural Networks for Matlab

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Convolutional Neural Networks for Matlab, including Invariang Backpropagation algorithm (IBP). Has versions for GPU and CPU, written on CUDA, C++ and Matlab. All versions work identically. The GPU version uses kernels from Alex Krizhevsky’s library ‘cuda-convnet2’.

Convolutional neural net is a type of deep learning classification algorithms, that can learn useful features from raw data by themselves. Learning is performed by tuning its weighs. CNNs consist of several layers, that are usually convolutional and subsampling layers following each other. Convolution layer performs filtering of its input with a small matrix of weights and applies some non-linear function to the result. Subsampling layer does not contain weights and simply reduces the size of its input by averaging of max-pooling operation. The last layer is fully connected by weights with all outputs of the previous layer. The output is also modified by a non-linear function. If your neural net consists of only fully connected layers, you get a classic neural net.

Learning process consists of 2 steps: forward and backward passes, that repeat for all objects in a training set. On the forward pass each layer transforms the output from the previous layer according to its function. The output of the last layer is compared with the label values and the total error is computed. On the backward pass the corresponding transformation happens with the derivatives of error with respect to outputs and weights of this layer. After the backward pass finished, the weights are changed in the direction that decreases the total error. This process is performed for a batch of objects simultaneously, in order to decrease the sample bias. After all the object have been processed, the process might repeat for different batch splits.

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