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Harjot Kaur
Harjot Kaur

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Published in Towards AI

·Oct 19, 2022

Why Should Euclidean Distance Not Be the Default Distance Measure?

The sustained development of technologies, data storage resources, and computing resources, gives rise to the production, storage, and processing of an exponentially growing volume of data. Typically, data mining applications involve the handling of this huge amount of high-dimensional data. The major techniques used in data mining rely on the…

Distance Metric

4 min read

Why Should Euclidean Distance Not Be the Default Distance Measure?
Why Should Euclidean Distance Not Be the Default Distance Measure?
Distance Metric

4 min read


Published in Towards AI

·Sep 9, 2022

Elucidating the Power of Inferential Statistics To Make Smarter Decisions!

The lost importance of inferential statistics… The strategic role of data science teams in the industry is fundamentally to help businesses to make smarter decisions. This includes decisions on minuscule scales (such as optimizing marketing spending) as well as singular, monumental decisions made by businesses (such as how to position…

Inferential Statistics

8 min read

Elucidating the power of Inferential Statistics to make smarter decisions!
Elucidating the power of Inferential Statistics to make smarter decisions!
Inferential Statistics

8 min read


Published in Towards AI

·Aug 30, 2022

The Mathematical Relationship between Model Complexity and Bias-Variance Dilemma

Most data science enthusiasts will concur with the claim - the ‘Bias-variance dilemma suffers from analysis paralysis’, as there is an immense literature on the concept of Bias-Variance, its decomposition, derivation, and its relationship with model complexity. Perhaps, we have crammed to the best of our abilities that simple models…

Bias Variance Tradeoff

5 min read

The Mathematical Relationship between Model Complexity and Bias-Variance Dilemma
The Mathematical Relationship between Model Complexity and Bias-Variance Dilemma
Bias Variance Tradeoff

5 min read


Aug 24, 2022

Why should you prefer SVD over EIG while performing Linear Principal Component Analysis?

Lets deliberate! Simply put, amongst many other applications of Principal Component Analysis (PCA), the main purpose is to identify patterns to reduce dimensions of the dataset with minimal loss of information. Usually, PCA is explained through an Eigen decomposition of the covariance matrix. …

Svd

3 min read

Why should you prefer SVD over EIG while performing Linear Principal Component Analysis?
Why should you prefer SVD over EIG while performing Linear Principal Component Analysis?
Svd

3 min read


Published in Towards AI

·Aug 20, 2022

Why Should Adam Optimizer Not Be the Default Learning Algorithm?

An increasing share of deep learning practitioners are training their models with adaptive gradient methods due to their rapid training time. Adam, in particular, has become the default algorithm used across many deep learning frameworks. Despite superior training outcomes, Adam and other adaptive optimization methods are known to generalize poorly…

Optimization Algorithms

4 min read

Why Should Adam Optimizer Not Be the Default Learning Algorithm?
Why Should Adam Optimizer Not Be the Default Learning Algorithm?
Optimization Algorithms

4 min read


Aug 11, 2022

Understanding Backpropagation using Mountaineering as an analogy

Intuition behind the algorithm! Backpropagation algorithm is the workhorse of learning in the neural networks. It is an expression for the partial derivative(∂L/∂w) of the loss function(∂L) with respect to any weight w (or bias b) in the network. The expression tells us how quickly the loss changes when we…

Backpropagation

4 min read

Understanding Backpropagation using Mountaineering as an Analogy
Understanding Backpropagation using Mountaineering as an Analogy
Backpropagation

4 min read


Aug 6, 2022

Math behind the Gradient Descent Algorithm

Heads up: This piece is for folks with basic understanding around the ML setup and possess some knowledge of derivatives. Why do we care about the Gradient descent algorithm? As we know, supervised machine learning algorithms are described as learning a target function (f) that best maps input variables (x) to an output variable (Y). Y = f(x) Given…

Gradient Descent

5 min read

Math behind the Gradient Descent Algorithm
Math behind the Gradient Descent Algorithm
Gradient Descent

5 min read


Nov 9, 2020

Text Generation using Markov Chain Algorithm

Introduction Markov Chain is a mathematical model of stochastic process that predicts the condition of the next state based on the condition of the previous one. Mathematically speaking, the conditional probability distribution of the next state depends on the current state and not the past states. That is s(t) depends only…

Markov Chains

4 min read

Text Generation using Markov Chain Algorithm
Text Generation using Markov Chain Algorithm
Markov Chains

4 min read


Published in Towards AI

·Sep 23, 2020

Solving SUDOKU with Binary Integer Linear Programming(BILP)

Background Sudoku is a logic-based puzzle that first appeared in the U.S. under the title “Number Place” in 1979 in the magazine Dell Pencil Puzzles & Word Games [6]. The game was designed by Howard Garns, an architect who, upon retirement, turned to puzzle creation. In the 1980s, the game grew…

Integer Programming

5 min read

Solving SUDOKU with Binary Integer Linear Programming(BILP)
Solving SUDOKU with Binary Integer Linear Programming(BILP)
Integer Programming

5 min read


Sep 1, 2020

PREDICTING BINARY CLASS PROBABILITY WITH LOGISTIC REGRESSION

Using Logistic Regression

Logistic Regression

2 min read

PREDICTING BINARY CLASS PROBABILITY
PREDICTING BINARY CLASS PROBABILITY
Logistic Regression

2 min read

Harjot Kaur

Harjot Kaur

62 Followers

Experienced Product Manager & Analytics Specialist | Visit my Analytics Portfolio @ harjotdadhwal.github.io

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