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Large Language Models are Zero-Shot Rankers for Recommender Systems
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Semantic Product Search for Matching Structured Product Catalogs in E-Commerce
What is More Likely to Happen Next
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Dynamic Programming (DP) in python
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Odds
One Class Classification
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Reno Talk @UMBC on Scale-2024
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Saddle Points
scalar
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Self Attention vs. Cross Attention
Self-Supervised Learning
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SentencePiece Tokenization
Sequence-to-Sequence Model
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Simple Linear Regression
Singular Value Decomposition (SVD)
Skip Gram Model
SMOTE
Soft Margin in SVM
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Some Common Behavioral Questions
Sources of Uncertainty
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Specificity
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Standard deviation
Standardization or Normalization
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Statistical Power
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Stochastic Gradient Descent (SGD)
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Stop Words
Stratified K Fold Cross Validation
Stride in CNN
Stump
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Sub-word Tokenizer
Supervised Learning
Support Vector Machine (SVM)
Support Vector
Surprise
SVC
Swallow vs. Deep Learning
t-SNE
Tanh
Text Preprocessing
TF-IDF
Three Way Partioning
Time Complexity of ML Algos
Time Complexity of ML Models
Tokenizer
Top-K in Retrieval System
trace-operator
Training a Deep Neural Network
Transformer
Triplet Loss
True Negative Rate
True Positive Rate
Two Pointer
Type 1 Error vs. Type 2 Error
Undercomplete Autoencoder
Undersampling
Uniform Distribution
Unigram Tokenization
unit-vector
Unsupervised Learning
Vanishing Gradient
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Variational Autoencoder
vector
Weakly Supervised Learning
Weight Initialization
Word Embeddings
Word Tokenizer
Word2Vec Embedding
WordPiece Tokenization
XGBoost
Z-score
Domain vs. Codomain vs. Range
#math
Domain vs. Codomain vs. Range
For a function,
y
=
f
(
x
)
x is the domain
y is the codomain
and all possible y for f is the range
Related Notes
Gradient
Integration by Parts or Integration of Product
Second Order Derivative or Hessian Matrix
Eigendecomposition
Convex vs Nonconvex Function
