Neural networks - Samenvattingen en Aantekeningen

This is a summary of the paper Supervised learning based on temporal coding in spiking neural networks for the course Seminar of Computer Vision by Deep Learning in TU Delft

This document contains a summary with all the necessary functions for you to know for the exam. It includes the notes made by the teacher in the online lectures that are not on the slides. It only includes the first part of the Data Fusion lecture. And there is nothing about How to Lie with Data, because I feel like this is just common sense.

Extensive summary of the course Mind and Machine. In this summary extra attention is paid to explaining the more complex concepts in detail & I included figures! As this course is not connected to a book or other literature, this summary is based on the lecture content, slides, assignments and documentaries. I personally really enjoyed this course and received a 9.5 on the exam, so it's safe to say this summary covers the course contents. Please do me a favor and leave a review! <3

Lecture notes and summary

This documents contains a summary of the final modules/weeks (4-7) for the course Data Mining for Business and Governance. The following topics are included in this summary: ⋅ Crisp (K-means) clustering ⋅ Fuzzy (c-means) clustering ⋅ Hierarchical clustering ⋅ Text mining ⋅ Preprocessing noisy text ⋅ Document similarity: Jaccard coefficient ⋅ Term frequency, inverse term frequency ⋅ Dimensionality reduction ⋅ Feature selection ⋅ Filtering strategy ⋅ Wrapper s...

This is a summary of all lectures for the Minor course, "Intro to Data Science"

Summary for the Data Mining course of the Master Data Science and Entrepreneurship.

Data Science samenvatting. Ik heb deze samenvatting gemaakt om te leren voor het vak Data Science dat in het 3de jaar wordt gegeve. Op basis van het lesmateriaal van Universiteit Leiden. Het is een uitgebreide samenvatting + key concepts (een nog compactere versie van dezelfde stof)

Introduction to Data Science samenvatting voor de Data Science Minor aan de VU. Informatie over Data Science, Introduction to Data Science, Artificial Intelligence, Computer Science, Machine learning basics, Linear Regression, Feature Engineering, Tree Based Methods, Model Validation, Neural networks, Fairness and Bias, Classification and Clustering

This is an extended summary of all lectures for the course Data Analytics for Business Intelligence (1BM110). This 50-page document (with a clickable table of contents for easier navigation) summarizes the essence of all topics covered in the course (as far as I could imagine when writing it). It includes as many images & visualizations as possible to clarify the concepts as much as possible, and make them easy to understand.