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Normalization Vs Standardization

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Normalization typically means rescales the values into a range of [0,1]. Standardization typically means rescales data to have a mean of 0 and a standard deviation of 1 (unit variance). MIN-MAX NORMALIZATION Min-max normalization is one of the most common ways to normalize data. For every feature, the minimum value of that feature gets transformed into a 0, the maximum value gets transformed into a 1, and every other value gets transformed into a decimal between 0 and 1. \frac{value - min}{max - min} Min-max normalization has one fairly significant downside: it does not handle outliers very well. Z-SCORE NORMALIZATION/STANDARDIZATION Z-score normalization is a strategy of normalizing data that avoids this outlier issue. Min-max normalization: Guarantees all features will have the exact same scale but does not handle outliers well. Z-score normalization: Handles outliers, but does not produce normalized data with the exact same scale. Normalization is good to use when you know that t...
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Dimensionality Reduction

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Dimensionality reduction is the process of reducing the number of random variables under consideration, by obtaining a set of principal variables. Components of Dimensionality Reduction There are two components of dimensionality reduction: Feature selection:  In this, we try to find a subset of the original set of variables, or features, to get a smaller subset that can be used to model the problem. It usually involves three ways: Filter Wrapper Embedded Feature extraction:  This reduces the data in a high dimensional space to a lower dimension space, i.e. a space with a lesser no. of dimensions. The various methods used for dimensionality reduction include: Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Generalized Discriminant Analysis (GDA) Dimensionality reduction may be both linear or non-linear, depending upon the method used. Principal Component Analysis(PCA) Condition - W hile the data in a higher-dimensional space is mapped to data in a lower d...
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ACTIVATION FUNCTION

Activation function decides, whether a neuron should be activated or not by calculating weighted sum and further adding bias with it. The purpose of the activation function is to introduce non-linearity into the output of a neuron. In a neural network, we would update the weights and biases of the neurons on the basis of the error at the output. This process is known as back-propagation. A neural network without an activation function is essentially just a linear regression model.   https://www.geeksforgeeks.org/activation-functions-neural-networks/ https://www.geeksforgeeks.org/activation-functions/?ref=rp https://towardsdatascience.com/everything-you-need-to-know-about-activation-functions-in-deep-learning-models-84ba9f82c253#:~:text=Simply%20put%2C%20an%20activation%20function,fired%20to%20the%20next%20neuron.
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GRADIENT DESCENT

https://www.geeksforgeeks.org/gradient-descent-algorithm-and-its-variants/?ref=lbp   https://towardsdatascience.com/batch-mini-batch-stochastic-gradient-descent-7a62ecba642a
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