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Arpit Bhayani Blogs

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Arpit Bhayani Blogs
001 Ai Topological Sort

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TLDR: MinHash - Fast Jaccard Similarity at Scale

Date: 2020-11-08 Source: https://arpitbhayani.me/blogs/jaccard-minhash

Overview

Learn Jaccard Similarity and MinHash, a technique to efficiently estimate set similarity at scale for tasks like near-duplicate detection. Set similarity measure finds its application spanning the Computer Science spectrum; some applications being - user segmentation, finding near-duplicate webpages/documents, clustering, recommendation generation, sequence alignment, and many more.

Key Points

  • Set similarity measure finds its application spanning the Computer Science spectrum; some applications being - user segmentation, finding near-duplicate webpages/documents, clustering, recommendation generation, sequence alignment, and many more.
  • Jaccard Similarity Coefficient as Probability: Jaccard Coefficient can also be interpreted as the probability that an element picked at random from the universal set U is present in both sets A and B. !https://user-images.githubusercontent.com/4745789/98462221-8dc3bd00-21d8-11eb-95bf-5a9267e88b97.png Another analogy for this probability is the chances of throwing a dart and it hitting the intersection.
001-ai-topological-sort.md
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002 Temporal Primer
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003 Rag Production
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004 Structure Of Llm Chat
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005 How Llms Work
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006 Monolith Is Distributed System
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007 Defensive Databases
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008 Bm25
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009 Join Algorithms
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010 Venting At Work
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011 Half Life
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012 Multi Paxos
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013 Mysql Replication Internals
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014 Bloom Filters
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015 Clock Sync Nightmare
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016 Kafka Partitions
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017 Product Quantization
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018 Qkv Matrices
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019 Deleted Production
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020 How Llm Inference Works
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021 Blocking Queues
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022 Heartbeats In Distributed Systems
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023 Cassandra Writes
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024 Redis Replication
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025 Arrogant People At Work
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026 Cdn Content Replication
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027 Cant Fix Everything Day One
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028 Emotions At Work
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029 Grpc Http2
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030 Meetings With No Agenda Are A Waste Of Time
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031 Growth Is Not About Doing Everything
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032 Career Longevity Vs Job Hopping
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033 Stay Relevant At Higher Salary Levels
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034 Why Consensus
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035 Database Deadlocks
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036 Cpu Cache Locality
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037 Eventual Consistency
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038 Dns Udp Tcp
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039 Masters
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040 Empathy Makes Great Engineers Unstoppable
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041 Good Mentors Build People
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042 Always Have Back Burner Projects
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043 Before You Push Back Know What Youre Standing On
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044 Be The One They Can Count On
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045 How Much People Bet On You
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046 How To Get Leadership To Say Yes To Your Project
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047 Dont Let Your Best Ideas Die In Silence
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048 Be Someone Others Want To Work With
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049 Dont Fall For Xy Problem Ask Right Questions
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050 Biggest Lie Startups Tell Engineers
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051 Promotions Are Proactive Not Reactive
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052 Not Enough To Be Right Learn To Be Heard
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053 No One Ships Alone
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054 Not Every Mistake Needs A Correction
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055 Build Influence At Work
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056 Your Soft Skills Arent Soft At All
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057 Experience Before Forming Opinion
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058 Curiosity And High Bias For Action
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059 Worklog
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060 Mistakes And Growth
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061 Own It Instead Of Sweeping It Aside
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062 Dont Wait Step Up
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063 Temporary Fix Is Permanent
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064 Interview Bias And What Sets You Apart
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065 Saying This Isnt My Problem Is A Problem
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066 Okr
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067 Miscommunication
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068 When In Doubt Code It Out
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070 Lead Projects That Land
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071 Abstract Thinking Skill Next Decade
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072 We Engineers Suck At Task Estimation
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073 Shiny Object Syndrome In Tech
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074 3p
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075 Leverage The Equilibrium
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076 On Demand Container Loading In Aws Lambda
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077 Sql Has Problems We Can Fix Them Pipe Syntax In Sql
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078 Nanolog A Nanosecond Scale Logging System
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079 Best Resource Is Mythical
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080 Wtf The Who To Follow Service At Twitter
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081 Know A Lot
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082 Out Of Syllabus
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083 Negotiate The Offer
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084 Never Bad Mouth Your Ex Exployer
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085 Culture Fit
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086 Quantification In Resume
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087 Hiring Is Unfair
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088 Questions For Interviewers
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089 Collaboration Communication
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090 Out Of Vicious Interview Cycle
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091 Pitch Projects Not Ideas
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092 Read Design Docs
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093 Read Rca Docs
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094 Start Generalist
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095 Do Not Rely On Summaries
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096 Structure Your Design Interviews
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097 Title Inflation
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098 Find Your Own Project
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099 Six Pointers To Crack Coding And Design Interviews
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100 Keep Yourself Unblocked
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101 Genetic Knapsack
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102 Pseudorandom Number Generation Lfsr
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103 How Indexes Work On Partitioned And Sharded Data
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104 Some Data Partitioning Strategies For Distributed Data Stores
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105 Data Partitioning
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106 Leaderless Replication
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107 Conflict Resolution
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108 Conflict Detection
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109 Multi Master Replication
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110 Monotonic Reads
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111 Read Your Write Consistency
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112 Handling Outages Master Replica
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113 Replication Formats
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114 Replication Strategies
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115 Master Replica Replication
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116 Durability
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117 Isolation
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118 Atomicity
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119 Consistency
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120 Architectures In Distributed Systems
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121 Mistaken Beliefs Of Distributed Systems
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122 Fork Bomb
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123 Chained Operators Python
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124 Taxonomy On Sql
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125 The Weird Walrus
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126 Fully Persistent Arrays
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127 Persistent Data Structures Introduction
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128 Constant Folding Python
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129 String Interning Python
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130 Recursion Visualizer Python
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131 Flajolet Martin
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132 2q Cache
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133 Israeli Queues
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134 1d Terrain
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135 Jaccard Minhash
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136 Ts Smoothing
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137 Lfu
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138 Morris Counter
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142 10x Engineer
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144 Decipher Single Xor
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145 Python Iterable Integers
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146 Inheritance C
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147 Rum
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148 Consistent Hashing
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149 Python Caches Integers
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150 Fractional Cascading
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151 Copy On Write
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153 Fsm Python
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154 Bayesian Average
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155 Sliding Window Ratelimiter
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156 Idf
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157 Better Programmer
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158 Python Prompts
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159 Rule 30 Cellular Automata
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160 Function Overloading
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161 Isolation Forest
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162 Image Steganography
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163 Long Integers Python
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164 I Changed My Python
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165 Benchmark And Compare Pagination Approach In Mongodb
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166 Mongodb Cursor Skip Is Slow
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167 Fast And Efficient Pagination In Mongodb
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168 Making Http Requests Using Netcat
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  • Problem at Scale: Computing Jaccard Similarity Coefficient is very simple, all we require is a union operation and an intersection operation on the participating sets.
  • Computing single MinHash: MinHash h of the set S is the index of the first element, from a permuted Universal Set, that is present in the set S.
  • A surprising property of MinHash: MinHash has a surprising property, according to which, the probability that the MinHash of random permutation produces the same value for the two sets equals the Jaccard Similarity Coefficient of those sets. !https://user-images.githubusercontent.com/4745789/98463732-8229c380-21e3-11eb-9b26-04ec08bc8753.png The above equality holds true because the probability of MinHash of two sets to be the same is the number of elements present in both the sets divided by the total number of elements in both the sets combined; which in fact is the definition of Jaccard Similarity Coefficient.
  • MinHash Signature: MinHash Signature of a set S is a collection of k MinHash values corresponding to k different MinHash functions.

    • Takeaway

    MinHash Signature of a set S is a collection of k MinHash values corresponding to k different MinHash functions.