Categories: Frame an ML Problem | High-Impact and Low-Cost
This post explores the process of identifying whether a business problem can be effectively solved using machine learning. It delves into key considerations, such as impact and cost, and provides g...
This guide provides a step-by-step tutorial on constructing a translation model using the Transformer architecture. We will code the encoder and decoder, train the model, save checkpoints, and perf...
A exploration of linear and non-linear decision boundaries in binary classification, focusing on the perceptron algorithm and the kernel trick for transforming non-linearly separable data into a hi...
This post provides an in-depth look at the difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) Estimation using concrete examples
Tokenization is the process of breaking down text into smaller units called tokens. In the context of the Byte Pair Encoding (BPE) algorithm, tokenization involves splitting words into subword unit...
This post provides an in-depth look at various regression techniques, including parametric and non-parametric regression, linear regression, Lasso and Ridge regression, logistic regression, and ker...
This post provides an in-depth look at various regression techniques, including parametric and non-parametric regression, linear regression, Lasso and Ridge regression, logistic regression, and ker...
This post explores the concepts of Nearest Neighbors (k-NN) and Decision Tree algorithms, including their pros and cons, and how to measure uncertainty using Gini impurity and entropy. A detailed e...
Machine Learning is the study of making machines learn a concept without explicitly programming it. It involves building algorithms that can learn from input data to make predictions or find patter...
It delves into the concepts of discrete and continuous random variables, joint distributions, independence, and conditional independence. It provides a thorough understanding of how these elements ...