📒
Machine & Deep Learning Compendium
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📒
Machine & Deep Learning Compendium
  • The Machine & Deep Learning Compendium
  • The Ops Compendium
    • Overview
    • Model Families
    • Weakly Supervised
    • Semi Supervised
    • Active Learning
    • Online Learning
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    • 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
    • Algorithms 101
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    • Probabilistic, Regression
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    • Dimensionality Reduction Methods
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    • Learning Classifier Systems
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    • Timeseries
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    • Digital Signal Processing (DSP)
    • Propensity Score Matching
    • Diffusion models
    • Graph Theory
    • Social Network Analysis
    • 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
    • Basics
    • Terminology
    • Feature Engineering
    • Deep Neural Audio
    • Algorithms
    • 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
    • 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
    • Design Of Experiments
    • DOE Tools
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    • Multi Armed Bandits
    • Contextual Bandits
    • Factorial Design
    • 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
    • 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)
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For the complete documentation index, see llms.txt. This page is also available as Markdown.
  1. Generative AI

Fairness, Accountability, and Transparency In Prompts

Debiasing using prompts

  1. MsPrompt: Multi-step Prompt Learning for Debiasing Few-shot Event Detection

  2. Debiasing Vision-Language Models via Biased Prompts

  3. Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts

  4. A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learning

  5. Debiasing Scores and Prompts of 2D Diffusion for Robust Text-to-3D Generation

  6. (good) Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing

  7. Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

Hallucinations

  1. (very good) understanding LLM hallucinations

PreviousPromptNextLarge Language Models (LLMs)

Last updated 2 years ago

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  • Debiasing using prompts
  • Hallucinations

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