Ai Cheat Sheet
Search…
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
Powered By
GitBook
L1 and L2 Loss Function
L1 and L2 are two loss functions in machine learning which are used to minimize the error.
L1 Loss function stands for Least Absolute Deviations.
Also known as LAD.
L
1
L
o
s
s
F
u
n
c
t
i
o
n
=
∑
i
=
1
n
∣
y
t
r
u
e
−
y
p
r
e
d
i
c
t
e
d
∣
L1LossFunction = \sum_{i=1}^n |y_{true}-y_{predicted}|
L
1
L
oss
F
u
n
c
t
i
o
n
=
∑
i
=
1
n
∣
y
t
r
u
e
−
y
p
re
d
i
c
t
e
d
∣
L2 Loss function stands for Least Square Errors.
Also known as LS.
L
2
L
o
s
s
F
u
n
c
t
i
o
n
=
∑
i
=
1
n
(
y
t
r
u
e
−
y
p
r
e
d
i
c
t
e
d
)
2
L2LossFunction = \sum_{i=1}^n (y_{true}-y_{predicted})^2
L
2
L
oss
F
u
n
c
t
i
o
n
=
∑
i
=
1
n
(
y
t
r
u
e
−
y
p
re
d
i
c
t
e
d
)
2
What Are L1 and L2 Loss Functions?
after_academy
Vector and Matrix - Previous
Matrix
Next - Machine Learning ↓↑
Linear Regression
Last modified
9mo ago
Copy link