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…

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…

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. …

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…

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…

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…

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…

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…

Using Logistic Regression