Technical Notes
Introduction
1.
Open Tech Challenges
2.
For Backend Programmers
2.1.
Coding Principles
2.2.
Design Patterns
2.3.
Restful API Design
2.4.
Entity Modeling
2.5.
Profiling
2.6.
Memory Management
2.7.
Java NIO
2.8.
IO Architecture
2.9.
Concurrent Programming
2.10.
Functional Programming
2.11.
Microservice
2.12.
Web Security
2.13.
Git Flow
2.14.
Quick Prototyping
2.15.
Interview Questions
2.16.
Valuable Experience Learnt
3.
Machine Learning
3.1.
Neural Network
3.2.
Deep Learning - Part 1
3.3.
Deep Learning - Part 2
3.4.
Data Aggregation
3.5.
Data Processing
3.6.
Regression
3.7.
Decision Tree
3.8.
SVM
3.9.
Bayesian Network
3.10.
Random Forest
3.11.
Machine Learning at Scale
4.
Data Structure/ Algorithm
4.1.
Recursion
4.2.
Algorithm
4.3.
B Tree vs B+ Tree Index
4.4.
Trie
4.5.
Graph
5.
Big Data Processing
5.1.
Scalable Design
5.2.
Locality Sensitivity Hashing
5.3.
Data Mining Lecture
5.4.
Consistent Hashing
5.5.
Consensus Algorithms
5.6.
Vert.x
5.7.
Redis
5.8.
Storm
5.9.
Kafka
5.10.
Hadoop
5.11.
Cascading
5.12.
Hive
5.13.
Spark
6.
Search
6.1.
Logstash
6.2.
ElasticSearch Videos
6.3.
What Makes ElasticSearch So Fast
6.4.
ElasticSearch In Production
6.5.
ElasticSearch In AWS
6.6.
ElasticSearch Cluster Management
6.7.
ElasticSearch & JVM
6.8.
ElasticSearch on Cloud
6.9.
ElasticSearch - Modeling
6.10.
ElasticSearch vs Algolia
6.11.
ElasticSearch - Spatial Search
6.12.
ElasticSearch Cheatsheet
6.13.
Lucene 5 - What is news?
6.14.
Use Case: Github
6.15.
Use Case: OpenDNS
6.16.
Use Case: Loggly
6.17.
Use Case: LinkedIn Search Architecture on Lucene
7.
DevOps
7.1.
Virtualization
7.2.
Network Security
7.3.
Tools
7.4.
Log Processing
7.5.
Cloud Computing
7.6.
System Tuning
8.
Ad Tech
8.1.
Header Bidding
9.
Cheatsheets
9.1.
Linux Commands
9.2.
Markdown
9.3.
Wordpress
9.4.
MySQL
9.5.
Math: Probability
9.6.
Math: Linear Algebra
9.7.
Tools
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Technical Notes
Data Mining
Reference
http://infolab.stanford.edu/~ullman/mmds/ch9.pdf