#3 Image Classification with KNN & Linear Classifiers (Part 2)

In this lecture, we continue from Lecture 2, exploring K-Nearest Neighbor (KNN) and introducing Linear Classifiers for image classification. 🔹 Key Topics: Efficiency & limitations of Nearest Neighbor Approximate search (FAISS) KNN with L1 & L2 distance, and hyperparameter tuning CIFAR-10 dataset example Transition to Linear Classifiers: parametric approach, decision boundaries This session highlights the trade-offs between non-parametric (KNN) and parametric (Linear Classifier) methods, laying the foundation for deep learning. 👉 Next up: Deep Learning architectures and their link to these basics! #MachineLearning #DeepLearning #ComputerVision #ImageClassification