đź“’
Machine & Deep Learning Compendium
  • The Machine & Deep Learning Compendium
    • Thanks Page
  • The Ops Compendium
  • Types Of Machine Learning
    • Overview
    • Model Families
    • Weakly Supervised
    • Semi Supervised
    • Active Learning
    • Online Learning
    • N-Shot Learning
    • Unlearning
  • Foundation Knowledge
    • Data Science
    • Data Science Tools
    • Management
    • Project & Program Management
    • Data Science Management
    • Calculus
    • Probability & Statistics
    • Probability
    • Hypothesis Testing
    • Feature Types
    • Multi Label Classification
    • Distribution
    • Distribution Transformation
    • Normalization & Scaling
    • Regularization
    • Information Theory
    • Game Theory
    • Multi CPU Processing
    • Benchmarking
  • Validation & Evaluation
    • Features
    • Evaluation Metrics
    • Datasets
    • Dataset Confidence
    • Hyper Parameter Optimization
    • Training Strategies
    • Calibration
    • Datasets Reliability & Correctness
    • Data & Model Tests
    • Fairness, Accountability, and Transparency
    • Interpretable & Explainable AI (XAI)
    • Federated Learning
  • Machine Learning
    • Algorithms 101
    • Meta Learning (AutoML)
    • Probabilistic, Regression
    • Data Mining
    • Process Mining
    • Label Algorithms
    • Clustering Algorithms
    • Anomaly Detection
    • Decision Trees
    • Active Learning Algorithms
    • Linear Separator Algorithms
    • Regression
    • Ensembles
    • Reinforcement Learning
    • Incremental Learning
    • Dimensionality Reduction Methods
    • Genetic Algorithms & Genetic Programming
    • Learning Classifier Systems
    • Recommender Systems
    • Timeseries
    • Fourier Transform
    • Digital Signal Processing (DSP)
    • Propensity Score Matching
    • Diffusion models
  • Classical Graph Models
    • Graph Theory
    • Social Network Analysis
  • Deep Learning
    • Deep Neural Nets Basics
    • Deep Neural Frameworks
    • Embedding
    • Deep Learning Models
    • Deep Network Optimization
    • Attention
    • Deep Neural Machine Vision
    • Deep Neural Tabular
    • Deep Neural Time Series
  • Audio
    • Basics
    • Terminology
    • Feature Engineering
    • Deep Neural Audio
    • Algorithms
  • Natural Language Processing
    • A Reality Check
    • NLP Tools
    • Foundation NLP
    • Name Matching
    • String Matching
    • TF-IDF
    • Language Detection Identification Generation (NLD, NLI, NLG)
    • Topics Modeling
    • Named Entity Recognition (NER)
    • SEARCH
    • Neural NLP
    • Tokenization
    • Decoding Algorithms For NLP
    • Multi Language
    • Augmentation
    • Knowledge Graphs
    • Annotation & Disagreement
    • Sentiment Analysis
    • Question Answering
    • Summarization
    • Chat Bots
    • Conversation
  • Generative AI
    • Methods
    • Gen AI Industry
    • Speech
    • Prompt
    • Fairness, Accountability, and Transparency In Prompts
    • Large Language Models (LLMs)
    • Vision
    • GPT
    • Mix N Match
    • Diffusion Models
    • GenAI Applications
    • Agents
    • RAG
    • Chat UI/UX
  • Experimental Design
    • Design Of Experiments
    • DOE Tools
    • A/B Testing
    • Multi Armed Bandits
    • Contextual Bandits
    • Factorial Design
  • Business Domains
    • Follow the regularized leader
    • Growth
    • Root Cause Effects (RCE/RCA)
    • Log Parsing / Templatization
    • Fraud Detection
    • Life Time Value (LTV)
    • Survival Analysis
    • Propaganda Detection
    • NYC TAXI
    • Drug Discovery
    • Intent Recognition
    • Churn Prediction
    • Electronic Network Frequency Analysis
    • Marketing
  • Product Management
    • Expanding Your Data Science Skills
    • Product Vision & Strategy
    • Product / Program Managers
    • Product Management Resources
    • Product Tools
    • User Experience Design (UX)
    • Business
    • Marketing
    • Ideation
  • MLOps (www.OpsCompendium.com)
  • DataOps (www.OpsCompendium.com)
  • Humor
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Last updated 4 months ago

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Covering approximately 500 topics, the ML & DL Compendium includes summaries, links, and articles across a wide array of subjects, including LLMs. These range from modern machine learning algorithms and deep learning techniques to specialized areas like NLP, audio processing, computer vision (classic and deep), time-series analysis, anomaly detection, and graphs. It also deep dives into strategic themes like data science management, team building, and practical essentials like product management, design, and technology stacks from a data science perspective.

The ML & DL Compendium is completely open and now lives on (please star it!). Driven by my belief in knowledge-sharing and education, this project will always remain not-for-profit and free.

The Machine & Deep Learning Compendium began as a personal project—a curated list of resources I maintained in a private Google document for my own learning. That document has now evolved into this new interface, and I’m excited to share it as an educational tool to help others learn and connect with the brilliant authors I’ve summarized, quoted, and referenced.

I envision it as a go-to resource for learners of all levels—whether you're an industry data scientist, an academic, or just starting out. It’s designed to save you countless hours of searching and filtering through content, providing a streamlined path to invaluable authors and resources you can further support.

Many Thanks, Dr. Ori Cohen

Let’s work together to support the community, amplify the voices of authors, and democratize education! If you spot something that could be improved, feel free to contribute via or me directly.

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LogoGitHub - orico/www.mlcompendium.com: The Machine Learning & Deep Learning Compendium was a list of references in my private & single document, which I curated in order to expand my knowledge, it is now an open knowledge-sharing project compiled using Gitbook.GitHub
The ML & DL Compendium Official GitHub repo
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