Machine Learning

Table of Contents Fundamentals of Recommendation Systems Recommendation Engines and Their Types What Are Recommendation Engines? Content-Based Recommendations Collaborative Recommendations Hybrid Recommendations Evaluating Recommendation Systems Root Mean Square Error Precision@K Recall@K Mean Reciprocal Rank Recommendation Techniques Text Mining K-Nearest Neighbor…

Table of Contents Introduction to Causality in Machine Learning Correlation and Causation Case Study 1: A “Marvelous” Problem Scenario 1: A Direct Cause Scenario 2: Reversing the Cause and Effect Scenario 3: Investigating a Hidden Cause Causal Thinking Case Study…

Are you tired of hearing buzzwords like “Machine Learning”, “Large Language Models”, and “AGI” everywhere you go, without understanding what they actually mean? If so, you’ve come to the right place. At PyImageSearch, we specialize in breaking down these complex…

Table of Contents Scaling Kaggle Competitions Using XGBoost: Part 4 What Is XGBoost? Our Dummy Dataset Breaking Down the Math Configuring Your Development Environment Having Problems Configuring Your Development Environment? Applying XGBoost on a Problem Statement Applying XGBoost to Our…

Table of Contents Automatic Differentiation Part 2: Implementation Using Micrograd Introduction What Is a Neural Network? Having Problems Configuring Your Development Environment? About micrograd Imports and Setup The Value Class Addition Compute Gradient Multiplication Compute Gradient Power Compute Gradient Negation…

Table of Contents Scaling Kaggle Competitions Using XGBoost: Part 2 AdaBoost The Dataset Sample Weights Choosing the Right Feature Significance of a Stump Calculating the New Sample Weights Moving Forward: The Subsequent Stumps Piecing It Together Configuring Your Development Environment…

Our entire review of machine learning and neural networks thus far has been leading up to this point: understanding Convolutional Neural Networks (CNNs) and the role they play in deep learning. In traditional feedforward neural networks, each neuron in the…

CNN Building Blocks Neural networks accept an input image/feature vector (one input node for each entry) and transform it through a series of hidden layers, commonly using nonlinear activation functions. Each hidden layer is also made up of a set…

A common question I get asked is: Are Convolutional Neural Networks invariant to changes in translation, rotation, and scaling? Is that why they are such powerful image classifiers? To answer this question, we first need to discriminate between the individual…