Ai Cheat Sheet
  • Home
  • Statistics ↓↑
    • Types of Measure
    • Population and Sample
    • Outliers
    • Variance
    • Standard Deviation
    • Skewness
    • Percentiles
    • Deciles
    • Quartiles
    • Box and Whisker Plots
    • Correlation and Covariance
    • Hypothesis Test
    • P Value
    • Statistical Significance
    • Bootstrapping
    • Confidence Interval
    • Central Limit Theorem
    • F1 Score (F Measure)
    • ROC and AUC
    • Random Variable
    • Expected Value
    • Central Limit Theorem
  • Probability ↓↑
    • What is Probability
    • Joint Probability
    • Marginal Probability
    • Conditional Probability
    • Bayesian Statistics
    • Naive Bayes
  • Data Science ↓↑
    • Probability Distribution
    • Bernoulli Distribution
    • Uniform Distribution
    • Binomial Distribution
    • Poisson Distribution
    • Normal Distribution
    • T-SNE
  • Data Engineering ↓↑
    • Data Science vs Data Engineering
    • Data Architecture
    • Data Governance
    • Data Quality
    • Data Compliance
    • Business Intelligence
    • Data Modeling
    • Data Catalog
    • Data Cleaning
    • Data Format
      • Apache Avro
    • Tools
      • Data Fusion
      • Dataflow
      • Dataproc
      • BigQuery
    • Cloud Platforms
      • GCP
    • SQL
      • ACID
      • SQL Transaction
      • Query Optimization
    • Data Engineering Interview Questions
  • Vector and Matrix
    • Vector
    • Matrix
  • Machine Learning ↓↑
    • L1 and L2 Loss Function
    • Linear Regression
    • Logistic Regression
    • Naive Bayes Classifier
    • Resources
  • Deep Learning ↓↑
    • Neural Networks and Deep Learning
    • Improving Deep Neural Networks
    • Structuring Machine Learning Projects
    • Convolutional Neural Networks
    • Sequence Models
    • Bias
    • Activation Function
    • Softmax
    • Cross Entropy
  • Natural Language Processing ↓↑
    • Linguistics and NLP
    • Text Augmentation
    • CNN for NLP
    • Transformers
      • Implementation
  • Computer Vision ↓↑
    • Object Localization
    • Object Detection
    • Bounding Box Prediction
    • Evaluating Object Localization
    • Anchor Boxes
    • YOLO Algorithm
    • R-CNN
    • Face Recognition
  • Time Series
    • Resources
  • Reinforcement Learning
    • Reinforcement Learning
  • System Design
    • SW Diagramming
    • Feed
  • Tools
    • PyTorch
    • Tensorflow
    • Hugging Face
  • MLOps
    • Vertex AI
      • Dataset
      • Feature Store
      • Pipelines
      • Training
      • Experiments
      • Model Registry
      • Serving
        • Batch Predictions
        • Online Predictions
      • Metadata
      • Matching Engine
      • Monitoring and Alerting
  • Interview Questions ↓↑
    • Questions by Shared Experience
  • Contact
    • My Personal Website
Powered by GitBook
On this page

Was this helpful?

  1. Deep Learning ↓↑

Bias

PreviousSequence ModelsNextActivation Function

Last updated 4 years ago

Was this helpful?

Let's say we have the model as y = mx instead of the y = mx + c. Here, the model is having constraint to train itself and find a line which passes only through the origin. Many times for the given data, it is impossible for the algorithm to fit the model so that it passes through the origin.

Let's give some freedom to the algorithm by changing the model as mx + c instead of mx, so that the model can find a line which fits the given data.

Bias is a constant which helps the model in a way that it can fit best for the given data. In other words, Bias is a constant which gives freedom to perform best.

LogoWhat Is Bias In Artificial Neural Network?after_academy