Deep Learning Specialization - Coursera
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  • Introduction
  • Neural Networks and Deep Learning
    • Introduction to Deep Learning
    • Logistic Regression as a Neural Network (Neural Network Basics)
    • Shallow Neural Network
    • Deep Neural Network
  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
    • Practical Aspects of Deep Learning
    • Optimization Algorithms
    • Hyperparameter Tuning, Batch Normalization and Programming Frameworks
  • Structuring Machine Learning Projects
    • Introduction to ML Strategy
    • Setting Up Your Goal
    • Comparing to Human-Level Performance
    • Error Analysis
    • Mismatched Training and Dev/Test Set
    • Learning from Multiple Tasks
    • End-to-End Deep Learning
  • Convolutional Neural Networks
    • Foundations of Convolutional Neural Networks
    • Deep Convolutional Models: Case Studies
      • Classic Networks
      • ResNets
      • Inception
    • Advice for Using CNNs
    • Object Detection
      • Object Localization
      • Landmark Detection
      • Sliding Window Detection
      • The YOLO Algorithm
      • Intersection over Union
      • Non-Max Suppression
      • Anchor Boxes
      • Region Proposals
    • Face Recognition
      • One-Shot Learning
      • Siamese Network
      • Face Recognition as Binary Classification
    • Neural Style Transfer
  • Sequence Models
    • Recurrent Neural Networks
      • RNN Structure
      • Types of RNNs
      • Language Modeling
      • Vanishing Gradient Problem in RNNs
      • Gated Recurrent Units (GRUs)
      • Long Short-Term Memory Network (LSTM)
      • Bidirectional RNNs
    • Natural Language Processing & Word Embeddings
      • Introduction to Word Embeddings
      • Learning Word Embeddings: Word2Vec and GloVe
      • Applications using Word Embeddings
      • De-Biasing Word Embeddings
    • Sequence Models & Attention Mechanisms
      • Sequence to Sequence Architectures
        • Basic Models
        • Beam Search
        • Bleu Score
        • Attention Model
      • Speech Recognition
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  1. Sequence Models
  2. Natural Language Processing & Word Embeddings

De-Biasing Word Embeddings

Word embeddings can reflect the gender, ethnicity, age, sexual orientation and other biases of the text used to train the model.

For example, Man->Doctor, Woman->Nurse, which is not a correct mapping, and is biased based on gender.

The following is an overview of the steps to address this bias:

  1. Identify the bias direction

  2. Neutralize i.e. for every word that is not definitional (i.e. isn't defined to satisfy a given bias, for example, 'father' is defitional as it is only defined for the male gender, but 'doctor' is not definitional since it isn't defined for a particular gender), project it in the non-bias direction to get rid of bias

  3. Equalize the definitional words, i.e. make them equidistant from the non defitional words

PreviousApplications using Word EmbeddingsNextSequence Models & Attention Mechanisms

Last updated 4 years ago

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