Hashing | Hash based data structures | Probabilistic data structures
A Bloom filter is a space-efficient probabilistic data structure, conceived by in 1970, that is used to test whether an element is a member of a set. False positive matches are possible, but false negatives are not – in other words, a query returns either "possibly in set" or "definitely not in set". Elements can be added to the set, but not removed (though this can be addressed with the variant); the more items added, the larger the probability of false positives. Bloom proposed the technique for applications where the amount of source data would require an impractically large amount of memory if "conventional" error-free hashing techniques were applied. He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, out of which 90% follow simple hyphenation rules, but the remaining 10% require expensive disk accesses to retrieve specific hyphenation patterns. With sufficient core memory, an error-free hash could be used to eliminate all unnecessary disk accesses; on the other hand, with limited core memory, Bloom's technique uses a smaller hash area but still eliminates most unnecessary accesses. For example, a hash area only 15% of the size needed by an ideal error-free hash still eliminates 85% of the disk accesses. More generally, fewer than 10 bits per element are required for a 1% false positive probability, independent of the size or number of elements in the set. (Wikipedia).
For more information on Bloom Filters, check the Wikipedias: http://en.wikipedia.org/wiki/Bloom_filter , for special topics like "How to get around the 'no deletion' rule" and "How do I generate all of these different hash functions anyways?" For other questions, like "who taught you how
From playlist Software Development Lectures
What is Bloom Filter | Bloom Filtering Pattern | MapReduce Design Pattern Tutorial | Edureka
Watch Sample Class recording: http://www.edureka.co/mapreduce-design-patterns?utm_source=youtube&utm_medium=referral&utm_campaign=what-bloom-filter A Bloom filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set. The price pa
From playlist MapReduce Design Patterns Tutorial Videos
From playlist filter (less comfortable)
16 1 Bloom Filters The Basics 16 min
From playlist Algorithms 1
Why are bloom filters such useful data structures? How do they work, and what do they do? This video is an introduction to the bloom filter data structure: we'll explore what they are, how they work, and build an understanding for why they're so efficient.
From playlist Spanning Tree Favorites
Bloom Filters | Algorithms You Should Know #2 | Real-world Examples
Subscribe to our weekly system design newsletter: https://bit.ly/3tfAlYD Checkout our bestselling System Design Interview books: Volume 1: https://amzn.to/3Ou7gkd Volume 2: https://amzn.to/3HqGozy Digital version of System Design Interview books: https://bit.ly/3mlDSk9 The Secret Sauc
From playlist Algorithms You Should Know For System Design
Introduction to Frequency Selective Filtering
http://AllSignalProcessing.com for free e-book on frequency relationships and more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Separation of signals based on frequency content using lowpass, highpass, bandpass, etc filters. Filter g
From playlist Introduction to Filter Design
16 2 Bloom Filters Heuristic Analysis 13 min
From playlist Algorithms 1
Understanding Bloom Filter in Depth | Filtering on Hbase using MapReduce Filtering Pattern | Edureka
Watch Sample Class recording: http://www.edureka.co/mapreduce-design-patterns?utm_source=youtube&utm_medium=referral&utm_campaign=bloomfilter-depth MapReduce Design Pattern is a template for solving a common and general data manipulation problem with MapReduce. A pattern is not specific t
From playlist MapReduce Design Patterns Tutorial Videos
Kaggle Live-Coding: Efficiently find overlaps between test & train data | Kaggle
Join Kaggle Data Scientist Rachael as she works on data science projects! Today we'll be implementing the dataset overlap metrics from "Models are Unsupervised Multitask Learners" (Radford et al, unpublished). You can find a copy here: https://d4mucfpksywv.cloudfront.net/better-language-mo
From playlist Kaggle Live Coding | Kaggle
MIT MAS.S62 Cryptocurrency Engineering and Design, Spring 2018 Instructor: Tadge Dryja View the complete course: https://ocw.mit.edu/MAS-S62S18 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP61KHzhg3JIJdK08JLSlcLId Future developments including block / committed bloom
From playlist MIT MAS.S62 Cryptocurrency Engineering and Design, Spring 2018
!!Con 2016 - Don’t forget to sketch! Running with large datasets By Adam Marcus
Don’t forget to sketch! Running with large datasets By Adam Marcus Large datasets got you down? Have no fear! Make them small! Sketches are probabilistic data structures: they store a rough outline of a dataset in way less space than the dataset itself takes up. We'll sketch out three ske
From playlist !!Con 2016
Fixing The Graphics Of FINAL FANTASY XIV
I've spent the past two months hard at work developing ReShade and GShade shaders to improve the graphics of Final Fantasy XIV. In this video I go over my creative and developmental process from start to finish explaining the decisions I made, the major issues I came across and the theory
From playlist Render Repair
Ruby on Ales 2014 - You Got Math In My Ruby! (You Got Ruby In My Math!)
By Rein Henrichs Really? Math? With the boring formulas and definitions and proofs? Yes, math. But not that kind of math! The kind of math that challenges our creativity. The kind of math that explores the beautiful patterns that connect seemingly unrelated things in wonderful and surprisi
From playlist Ruby on Ales 2014
reaLD 3D glasses filter with a linear polarising filter
This is for a post on my blog: http://blog.stevemould.com
From playlist Everything in chronological order