Continue exploring the Perceptron algorithm in this comprehensive lecture that delves into advanced concepts including the mistake bound theorem, building upon previous foundational knowledge of neural network fundamentals. Gain deeper insights into this fundamental machine learning algorithm through detailed explanations and theoretical proofs over the 80-minute session. Access supplementary materials and detailed notes to reinforce understanding of this crucial topic in neural network architecture and machine learning theory.
Perceptron Algorithm and Mistake Bound Theorem - Lecture 9
Overview
Syllabus
Machine Learning: Lecture 9: Perceptron (continued)
Taught by
UofU Data Science
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Introduction to the Perceptron Algorithm - Lecture 9
