Ai Cheat Sheet
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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
Tools
Cloud Platforms
SQL
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
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
Interview Questions ↓↑
Questions by Shared Experience
Contact
My Personal Website
Powered By
GitBook
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 modified
2yr ago