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RNN. A recursive network is just a generalization of a recurrent network. In a recurrent network the weights are shared (and dimensionality remains ...

S1 Fig. Displays the actual data and the predicted data from the four models for each stock index in Year 2 from 2011.10.01 to 2012.09.30.

The above figure shows what's inside a LSTM block, in which, black arrows represent full matrices multiplications, dashed arrows represent weighted peephole ...

Gradient descent with small (top) and large (bottom) learning rates. Source: Andrew Ng's Machine Learning course on Coursera

There are two factors that affect the magnitude of gradients — the weights and the activation functions (or more precisely, their derivatives) that the ...

Each pixel is replaced by a weighted sum of the surrounding pixels. The neural network has to learn the weights. Picture from developer.apple.com.

This idea can be used in other network such as RNN as well. The figure below is borrowed from One Shot Learning with Siamese Networks in PyTorch – Hacker ...

... we compare this to neural network. A neural network is trained on a data. This network gains knowledge from this data, which is compiled as “ weights” of ...

Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles 1st Edition, Kindle Edition

LightNet is a lightweight, versatile and purely Matlab-based deep learning framework. The idea underlying its design is to provide an easy-to-understand, ...

Schematic description of the decomposition of trajectories and trajectory separation into recurrent and input components.

Neuroevolution is a method for optimizing neural network weights and topologies using evolutionary computation. It is particularly useful in sequential ...

Learning tested on an encoder-decoder architecture. The red curve shows sine waves as a function of network process iteration while the blue curve ...