Ai Cheat Sheet
Ai Cheat Sheet
Ai Cheat Sheet
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 Cleaning
Vector and Matrix
Vector
Matrix
Machine Learning ↓↑
L1 and L2 Loss Function
Linear Regression
Logistic Regression
Naive Bayes Classifier
Resources
Deep Learning ↓↑
Machine Learning vs Deep Learning
Bias
Activation Function
Softmax
Cross Entropy
Natural Language Processing ↓↑
Linguistics and NLP
Text Augmentation
CNN for NLP
Transformers
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
Feed
Interview Questions ↓↑
Questions by Shared Experience
Contact
My Personal Website
Face Recognition
One-Shot Learning is Not a Good Approach
Instead, Learn a Similarity Function
Siamese Network
Triplet Loss
Computer Vision ↓↑ - Previous
R-CNN
Next - Time Series
Resources
Last updated
6 months ago
Edit on GitHub
Contents
One-Shot Learning is Not a Good Approach
Instead, Learn a Similarity Function
Siamese Network
Triplet Loss