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  • Model
  • Thus, surprisingly, our simple neural model qualitatively recapitulates many diverse regularities underlying semantic development, while providing analytic insight into how the statistical structure of an environment can interact with nonlinear deep-learning dynamics to give rise to these regularities. (stanford.edu)
  • The presented model is a discrete-time, continuous-state Hopfield neural network and the states of the model are updated synchronously. (hindawi.com)
  • build
  • If I build a recurrent neural net into the head of a robot, I will say that the robot computes responses to stimuli, irrespective of whether the neural net is a continuous system or a discrete system approximating a continuous system. (blogspot.com)
  • To make our discussion concrete, we'll show how to build a neural network using Deeplearning4j, a popular open-source deep-learning library for the JVM. (infoq.com)
  • perform
  • Following a line is a well known problem in computer science, and there is no need for a neural network to perform such a task. (odsc.com)
  • full
  • Notice that the full structure of the neural net has to be predefined, and it is the weights themselves that can be continuously/semi-continuously (in batches) updated. (sflscientific.com)
  • Just as human memory circulates invisibly within a body, affecting our behavior without revealing its full shape, information circulates in the hidden states of recurrent nets. (skymind.ai)
  • structure
  • After the neural network structure is decided, the weights are randomly initialised (note, these should never be initialised to the exact same value, since you'll get into trouble with the conserved symmetry). (sflscientific.com)
  • Next
  • Seeing photograph of a cat will not lead the net to perceive an elephant next. (skymind.ai)