Posted in Machine Learning

The Transformer Attention Mechanism

Tweet Share Share Before the introduction of the Transformer model, the use of attention for neural machine translation was being implemented by RNN-based encoder-decoder architectures….

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Face Recognition using Principal Component Analysis

Tweet Share Share Last Updated on October 30, 2021 Recent advance in machine learning has made face recognition not a difficult problem. But in the…

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Using Singular Value Decomposition to Build a Recommender System

Tweet Share Share Last Updated on October 29, 2021 Singular value decomposition is a very popular linear algebra technique to break down a matrix into…

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A Gentle Introduction to Vector Space Models

Tweet Share Share Last Updated on October 23, 2021 Vector space models are to consider the relationship between data that are represented by vectors. It…

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Principal Component Analysis for Visualization

Tweet Share Share Last Updated on October 27, 2021 Principal component analysis (PCA) is an unsupervised machine learning technique. Perhaps the most popular use of…

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Further-Machine-Learning-Resources
Posted in Machine Learning

Further Machine Learning Resources

This section has been a fast visit through Machine Learning in Python, principally utilizing the devices inside the Scikit-Learn library. However long the section is,…

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A Gentle Introduction To Sigmoid Function
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A Gentle Introduction To Sigmoid Function

Tweet Share Share Whether you implement a neural network yourself or you use a built in library for neural network learning, it is of paramount…

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Calculus in Action: Neural Networks
Posted in Machine Learning

Calculus in Action: Neural Networks

An strained neural network is a computational model that approximates a mapping between inputs and outputs.  It is inspired by the structure of the human…

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A Gentle Introduction to Taylor Series
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A Gentle Introduction to Taylor Series

A Gentle Introduction to Taylor Series Taylor series expansion is an superstitious concept, not only the world of mathematics, but moreover in optimization theory, function…

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A Gentle Introduction To Approximation
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A Gentle Introduction To Approximation

  When it comes to machine learning tasks such as nomenclature or regression, propinquity techniques play a key role in learning from the data. Many…

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The Chain Rule of Calculus – Even More Functions

Tweet Share Share Last Updated on August 19, 2021 The uniting rule is an important derivative rule that allows us to work with composite functions….

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Line Search Optimization With Python

Tweet Share Share The line search is an optimization algorithm that can be used for objective functions with one or increasingly variables. It provides a…

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Gradient Descent With RMSProp from Scratch

Tweet Share Share Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of…

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Dual Annealing Optimization With Python

Tweet Share Share Dual Annealing is a stochastic global optimization algorithm. It is an implementation of the generalized simulated annealing algorithm, an extension of simulated…

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A Gentle Introduction to the BFGS Optimization Algorithm

Tweet Share Share The Broyden, Fletcher, Goldfarb, and Shanno, or BFGS Algorithm, is a local search optimization algorithm. It is a type of second-order optimization…

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Posted in Machine Learning

Essence of Bootstrap Aggregation Ensembles

Tweet Share Share Bootstrap aggregation, or bagging, is a popular ensemble method that fits a visualization tree on variegated bootstrap samples of the training dataset….

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