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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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ML Case Study or ML Design
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norm
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One Class Classification
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Pruning in Decision Tree
PyTorch Loss Functions
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Questions to ask in a Interview?
Quintile or Percentile
Quotient Rule or Differentiation of Division
R-squared Value
Radial Basis Kernel
Random Forest
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Recall@K
Recommender System (RecSys)
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Reinforcement Learning from Human Feedback (RLHF)
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Reno Talk @UMBC on Scale-2024
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ROUGE-L Score
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Saddle Points
scalar
Second Order Derivative or Hessian Matrix
Self Attention vs. Cross Attention
Self-Supervised Learning
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Sensitivity
SentencePiece Tokenization
Sequence-to-Sequence Model
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Sigmoid Kernel
Simple Linear Regression
Singular Value Decomposition (SVD)
Skip Gram Model
SMOTE
Soft Margin in SVM
Softmax
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Softsign
Some Common Behavioral Questions
Sources of Uncertainty
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spacy-doc-span-token
spacy-explanation-of-labels
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Specificity
Splitting tree in Decision Tree
Stacking or Meta Model in Ensemble Learning
Standard deviation
Standardization or Normalization
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Statistical Power
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Stemming
Stochastic Gradient Descent (SGD)
Stochastic Gradient Descent with Momentum
Stop Words
Stratified K Fold Cross Validation
Stride in CNN
Stump
Sub-sampling in Word2Vec
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
Variance
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
Differentiation
Euclidian Norm
Differentiation of Product
Chain Rule
