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Backpropagation is a repeated application of chain rule of calculus for partial derivatives.
The first step is to calculate the derivatives of the objective function with respect to the output units, then the derivatives of the output of the last hidden layer to the input of the last hidden layer; then the input of the last hidden layer to the weights between it and the penultimate hidden layer, etc. And here’s Yann Le Cun’s important paper on the subject.
The intent of this glossary is to provide clear definitions of the technical terms specific to deep artificial neural networks. An activation, or activation function, for a neural network is defined as the mapping of the input to the output via a non-linear transform function at each “node”, which is simply a locus of computation within the net.
Each layer in a neural net consists of many nodes, and the number of nodes in a layer is known as its width.
To backpropagate over many time steps, BPTT can be truncated for the purpose of efficiency.
Truncated BPTT limits the time steps over which error is propagated.
A restricted Boltzmann machine is a type of autoencoder, and in fact, autoencoders come in many flavors, including Variational Autoencoders, Denoising Autoencoders and Sequence Autoencoders.
Attention models “attend” to specific parts of an image in sequence, one after another.
A special form of backpropagation is called backpropagation through time, or BPTT, which is specifically useful for recurrent networks analyzing text and time series.
With BPTT, each time step of the RNN is the equivalent of a layer in a feed-forward network.
Those weights and biases are slowly updated as the neural net minimizes its error; i.e.
the level of nodes’ activation change in the course of learning.