Perceptron - Study guides, Class notes & Summaries

This is the summary that I used to study for the course Pattern Recognition. It contains all the relevant information from the lecture slides.

This document contains most asked question and answers on Machine Learning and Deep Learning to help the students prepare well for the University exams and focus on most important topics.

The use of the XOR gate in a perceptron, its operation and process

Very useful summary for the Machine Learning course at Tilburg University (Data Science and Society). Summary contains all parts that are discussed in the course (2019) extra examples, explanation and notes. Summary also contains many useful visualizations.

Summary Machine Learning, Tilburg University, 2021

Complete Handwritten Lecture Notes for ECE421 - Introduction to Machine Learning (2021 Winter)

Full summary including an introduction of Machine Learning and algorithms, such as Decision Tree, Perceptron, Gradient Descent, Logistic Regression (classifier) and Neural Networks. This summary also includes a section about Feature Engineering. Extra context and illustrations/graphs are also given, which makes this field of study a bit more understandable.

For the Machine Learning exam it is allowed to keep a cheat sheet of 1A4. This is specially made so that all subjects of this course are described in detail, such as Decision Trees, Perceptron, Gradient Descent, Feature Engineering, Logistic Regression and Neural Networks. You are allowed to bring cheat sheet (1A4) with you. On this cheat sheet, all the necessary information (and more) are available on just two sides of paper. The following subjects are included: Decision Trees, Perceptron, Grad...