One advantage of this type of filtering is that the location of the linear predictor's spectral peaks is entirely determined by the target signal, and can be as precise as allowed by the time period to be filtered.
22.
In reinforcement learning settings, there is no teacher providing target signals for the RNN, instead a fitness function or reward function is occasionally used to evaluate the RNN's performance, which is influencing its input stream through output units connected to actuators affecting the environment.
23.
In reinforcement learning settings, there is no teacher providing target signals for the RNN, instead a fitness function or reward function or utility function is occasionally used to evaluate the performance of the RNN, which is influencing its input stream through output units connected to actuators affecting the environment.
24.
Use a more systematic color scheme for the lines-right now there are various high & low frequency lines that appear red; even if the frequencies aren't meant to be the full visible color spectrum, a ROYGBIV sequence of colors would better express the idea that they're increasing frequencies; the line for the target signal should probably change color as it's shifted, to clarify what's happening.
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