Search results
Results From The WOW.Com Content Network
The vector consists of 0s in all cells with the exception of a single 1 in a cell used uniquely to identify the word. One-hot encoding ensures that machine learning does not assume that higher numbers are more important. For example, the value '8' is bigger than the value '1', but that does not make '8' more important than '1'.
A General encoder's block diagram. An encoder (or "simple encoder") in digital electronics is a one-hot to binary converter.That is, if there are 2 n input lines, and at most only one of them will ever be high, the binary code of this 'hot' line is produced on the n-bit output lines.
An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.
A convolutional encoder is a finite state machine. An encoder with n binary cells will have 2 n states. Imagine that the encoder (shown on Img.1, above) has '1' in the left memory cell (m 0), and '0' in the right one (m −1). (m 1 is not really a memory cell because it represents a current value). We will designate such a state as "10".
This encoding technique reduces the width of the combinational logic, and as a result, the state machine requires fewer levels of logic between registers, reducing its complexity and increasing its speed. In binary encoding, the number of bits (b) per state depends on number of states (n). The relationship is defined by the equation b = log 2 ...
Digital electronics is a field of electronics involving the study of digital signals and the engineering of devices that use or produce them. This is in contrast to analog electronics which work primarily with analog signals. Despite the name, digital electronics designs include important analog design considerations.
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).