Convolutional Neural Networks (CNNs) are a class of deep learning models that excel at working with image data. Instead of processing each pixel independently (like in a fully connected neural network), CNNs use filters (or kernels) to scan across the image, capturing spatial hierarchies and local patterns like edges, textures, and shapes.
Let's dive into logistic regression, a fundamental classification algorithm. We'll implement it from scratch, break down the code step by step, and demonstrate its application on a popular dataset.
Have you ever wondered how Netflix knows exactly what show you might want to watch next? Or how your email automatically filters spam messages? Or maybe how your phone recognizes your face? All of these technological wonders are powered by machine learning.
Have you ever wondered how an AI agent can learn to play a game—like Snake, Pac-Man, or even chess—just by trial and error? Behind the scenes, a powerful concept called Q-learning is often at work. Q-learning is a model-free reinforcement learning algorithm. That means the agent doesn’t need to know the rules of the environment—it learns them by experience.
Support Vector Machine (SVM) is a powerful supervised learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that best separates data points of different classes while maximizing the margin between them. SVM is especially effective in high-dimensional spaces and robust to outliers.