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Machine & Deep Learning Compendium
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The Machine & Deep Learning Compendium
Types Of Machine Learning
Data Science
Data Science Tools
Management
Data Science Management
Calculus
Probability & Statistics
Probability
Feature Types
Features
Calibration
Multi Label Classification
Distribution
Distribution Transformation
Information Theory
Game Theory
Datasets
Dataset Confidence
Normalization & Scaling
Regularization
Validation
Meta Learning
Examples
Evaluation Metrics
Benchmarking
Hyper Parameter Optimization
Multi CPU Processing
MLOps Monitoring & Alerts
Algorithms 101
Training Strategies
Classic Machine Learning
Label Algorithms
Clustering Algorithms
Anomaly Detection
Decision Trees
Active Learning Algorithms
Linear Separator Algorithms
Ensembles
Reinforcement Learning
Incremental Learning
Dimensionality Reduction Methods
Genetic Algorithms & Genetic Programming
Learning Classifier Systems
Recommender Systems
Timeseries
Fourier Transform
Digital Signal Processing (DSP)
Natural Language Processing
Natural Language Programming (Many Topics)
String Matching
Name Matching
Graphs
Graph Theory
Social Network Analysis
Deep Learning
Deep Neural Nets
Deep Neural Machine Vision
Deep Neural Audio
Business Domains
Growth
Root Cause Effects (RCE/RCA)
Log Parsing / Templatization
Fraud Detection
Additional DS Skills
Expanding Your Data Science Skills
Full-Stack & Ops
Business
Product Management
User Experience Design (UX)
Marketing
Humor
Data Engineering
Data Pipelines
Data Engineering
Lakes & Warehouses
Data Engineering Questions & Training
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The Machine & Deep Learning Compendium
Hi! Nearly a year ago I announced the Machine & Deep Learning Compendium, which was a personal list of resources curated in a private Google document, for my own education. That document is now retired in favor of this new interface. I decided to share it as an educational tool in order to allow people to learn and connect to all the great authors that I summarized, quoted, and referenced.
PLEASE NOTE: I'm in the midst of restructuring the compendium. I'm taking on this huge effort because the compendium started as my personal list of resources for my own private use without any standardization and without the intention of opening it up for the public, in order to allow readers to easily find topics and for referenced authors to get support back from the community. If you feel like something should be changed, content-wise, please create a PR or contact me and we'll work together to make that happen. My intent is to support both the community and authors and to democratise education.
The Compendium is fully open. It is now a project on GitBook & GitHub (please star it!). I believe in education and knowledge sharing and the compendium will always be not-for-profit and free. I see this compendium as a gateway, as a frequently visited resource for people of various proficiency levels, for industry data scientists, and academics. The compendium will save you countless hours googling and sifting through articles that may not give you any value, and for reaching great authors that you can support further.
The Compendium includes around 500 topics, that contain various summaries, links, and articles that I have read on numerous topics that I found interesting or that I had needed to learn. It includes the majority of modern machine learning algorithms, statistics, feature selection, and engineering techniques, deep-learning, NLP, audio, deep & classic vision, time-series, anomaly detection, graphs, experiment management, and much more. In addition to strategic topics such as data science management and team building, and essential topics such as product management, product design, and a technology stack from a DS POV.
Please keep in mind that this is a perpetual work in progress with a variety of topics. If you feel that something should be changed, you can now easily contribute using GitBook, GitHub, or contact me.
Please note that on GitHub you should push to master.
I would like to thank the following contributors: Samuel Jefroykin, Sefi Keller
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Types Of Machine Learning
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