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Nov 17, 2016 · Gnostice Document Studio .NET is the next-generation multi-format document-processing component suite for .NET developers. It supports ... Version: 2019.1.1 Report: Report Abuse Learning Rate The learning rate controls the size of each step toward the minimum of the objective function If the learning rate is too small, the weight adjustments are correspondingly small More learning iterations are then required to reach a local minimum However, the search path will closely approximate the gradient path Figure 75(a) illustrates the e ect of small On the other hand, large will have large weight updates Convergence will initially be fast, but the algorithm will eventually oscillate without reaching the minimum It is also possible that too large a learning rate will cause jumping over a good local minimum proceeding toward a bad local minimum Figure 75(b) illustrates the oscillating behavior, while Figure 75(c) illustrates how large learning rates may cause the network to overshoot a good minimum and get trapped in a bad local minimum Small learning rates also have the disadvantage of being trapped in a bad local minimum as illustrated in Figure 75(d) The search path goes down the rst local minimum, with no mechanism to move out of it toward the next, better minimum Of course, the search trajectory depends on the initial starting position If the second initial point is used, the NN will converge to the better local minimum But how should the value of the learning rate be selected One approach is to nd the optimal value of the learning rate through cross-validation, which is a lengthy process An alternative is to select a small value (eg 01) and to increase the value if convergence is too slow, or to decrease it if the error does not decrease fast enough Plaut et al [680] proposed that the learning rate should be inversely proportional to the fanin of a neuron This approach has been theoretically justi ed through an analysis of the eigenvalue distribution of the Hessian matrix of the objective function [167] Several heuristics have been developed to dynamically adjust the learning rate during training One of the simplest approaches is to assume that each weight has a di erent learning rate kj The following rule is then applied to each weight before that weight is updated: if the direction in which the error decreases at this weight change is the same as the direction in which it has been decreasing recently, then kj is increased; if not, kj is decreased [410] The direction in which the error decreases is determined by the sign of the partial derivative of the objective function with respect o the weight Usually, the average change over a number of pattern presentations is considered and not just the previous adjustment An alternative is to use an annealing schedule to gradually reduce a large learning rate to a smaller value (refer to equation 440) This allows for large initial steps, and ensures small steps in the region of the minimum. pdf viewer winforms c#: PDF viewer Control for winforms - MSDN - Microsoft winforms free pdf viewer WinForms PDF Viewer: Getting Started - YouTube
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May 17, 2019 · Use the DevExpress WinForms PDF Viewer Control to display PDF files directly in your WinForms application without the need to install an ... For example, given a sentence in the English language, such as the dog barked , we might transform the sentence into a sequence of (part-of-speech word) 2-tuples, ((DEFINITE_ARTICLE, "the"), (NOUN, "dog"), (VERB, "barked")) We would then perform syntactic analysis to see if this is a valid English sentence In this case it is, but our parser would have to reject, say, the barked dog The lexing phase is used to convert the data into a stream of tokens In typical cases, each token holds at least two pieces of information: the token s type (the kind of data or language construct being represented), and the token s value (which may be empty if the type stands for itself for example, a keyword in a programming language) The parsing phase is where a parser reads each token and performs some semantic action The parser operates according to a prede ned set of grammar rules that de ne the syntax that the data is expected to follow (If the data doesn t follow the syntax rules the parser will correctly fail) In multiphase parsers, the semantic action consists of building up an internal representation of the input in memory (called an Abstract Syntax Tree AST), which serves as input to the next phase Once the AST has been constructed, it can be traversed, for example, to query the data, or to write the data out in a different format, or to perform computations that correspond to the meanings encoded in the data Data formats and DSLs (and programming languages generally) can be described using a grammar a set of syntax rules that de ne what is valid syntax for the data or language Of course, just because a statement is syntactically valid doesn t mean that it makes sense for example, the cat ate democracy is syntactically valid English, but meaningless Nonetheless, being able to de ne the grammar is very useful, so much so that there is a commonly used syntax for describing grammars BNF (Backus Naur Form) Creating a BNF is the rst step to creating a parser, and although not formally necessary, for all but the most trivial grammars it should be considered essential Here we will describe a very simple subset of BNF syntax that is suf cient for our needs In a BNF there are two kinds of item: terminals and nonterminals A terminal is an item which is in its nal form, for example, a literal number or string A nonterminal is an item that is de ned in terms of zero or more other items (which themselves may be terminals or nonterminals) Every nonterminal must ultimately be de ned in terms of zero or more terminals Figure 141 shows an example BNF that de nes the syntax of a le of attributes , to put things into perspective. winforms pdf viewer .NET open PDF in winform without external dependencies - Stack ...
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