Think Logically
CNN stands for convolutional neural network. It is mostly used for developed for object recognition tasks such as handwritten digit recognition. There are four types of layers in a Convolutional Neural Network: 1. Convolutional Layers. 2. Pooling Layers. 3. Fully-Connected Layers. 4.Dropout layers. CNN is just a function which operates on another function in the below …
Gradient Descent Gradient Descent is an optimization algorithm that optimize the cost of the function.The goal is to continue to try different values for the coefficients, evaluate their cost and select new coefficients that have a slightly better (lower) cost. https://www.hackerearth.com/blog/machine-learning/3-types-gradient-descent-algorithms-small-large-data-sets/ https://medium.com/@zhaoyi0113/python-implementation-of-batch-gradient-descent-379fa19eb428 https://www.analyticsvidhya.com/blog/2017/03/introduction-to-gradient-descent-algorithm-along-its-variants/
In order to calculate score of different types of Algorithm we use following types of methods, few methods from SkLearn library are mentioned below. Scoring Function Comment Classification ‘accuracy’ metrics.accuracy_score ‘average_precision’ metrics.average_precision_score ‘f1’ metrics.f1_score for binary targets ‘f1_micro’ metrics.f1_score micro-averaged ‘f1_macro’ metrics.f1_score macro-averaged ‘f1_weighted’ metrics.f1_score weighted average ‘f1_samples’ metrics.f1_score by multilabel sample ‘neg_log_loss’ metrics.log_loss …
A class is a wrapping of data member and member function inside a single unit. Lets take an example of a Interest class in which we have members, principal, rate and time. We have member function as SimpleIntrest. class Interest(): rate=3.5 def __init__(self,p,t): #Data Members self.principal=p self.rate=self.rate self.time=t self.si=self.principal*self.rate*self.time/100 pass #Memeber Function def SimpleIntrest(self): print(self.si) …
Here general refers to the dependence on potentially more than one explanatory variable, v.s. the simple linear model : Some of the Generalized liner models are: 1-Ordinary Least Squares :Linear Regression fits a linear model with coefficients to minimize the residual sum of squares between the observed responses in the data set, and the responses …
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