{"title":"Perceptrons","authors":"Vinita Silaparasetty","doi":"10.1142/9789814343039_0007","DOIUrl":"https://doi.org/10.1142/9789814343039_0007","url":null,"abstract":"to determine it’s amount of “activation.” If this activiation is positive (i. ., a > 0) it predicts that this example is a positive example. Otherwise it predicts a negative example. The weights of this neuron are fairly easy to interpret. Suppose that a feature, for instance “is this a System’s class?” gets a zero weight. Then the activation is the same regardless of the value of this feature. So features with zero weight are ignored. Features with positi e weights are indi ative of positive examples because they cause the activation to increase. Features with negative weights are indicative of negative examples because they cause the activiation to decrease. What would happen if we encoded binary features like “is this a System’s class” as no=0 and yes= 1 (rather than the standard no=0 and yes=+1)? ? It is often convenient to have a non-zero threshold. In other words, we might want to predict positive if a > q for some value q. The way that is most convenient to achieve this is to introduce a bias term into the neuron, so that the activation is always increased by some fixed value b. Thus, we compute:","PeriodicalId":382150,"journal":{"name":"Deep Learning Projects Using TensorFlow 2","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121089150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras","authors":"Vinita Silaparasetty","doi":"10.1007/978-1-4842-5802-6","DOIUrl":"https://doi.org/10.1007/978-1-4842-5802-6","url":null,"abstract":"","PeriodicalId":382150,"journal":{"name":"Deep Learning Projects Using TensorFlow 2","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125699026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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