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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
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Dynamic Programming (DP) in python
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How to Choose Kernel in SVM
How to combine in Ensemble Learning
How to prepare for Behavioral Interview
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K Fold Cross Validation
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Kernel in SVM
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Logistic Regression
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Majority vote in Ensemble Learning
Manhattan Distance
Margin in SVM
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matplotlib functions
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max-norm
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Mean Squared Error (MSE)
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Mean
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Merge K-sorted List
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ML Case Study or ML Design
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Model Based vs. Instance Based Learning
MRR
Multi Class Cross Entropy
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Multi Layer Perceptron
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Mutual Information
N-gram Method
Naive Bayes
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Negative Sampling
Nesterov Accelerated Gradient (NAG)
Neural Network Normalization
Neural Network
Next Sentence Prediction
norm
Normal Distribution
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Null Hypothesis
Odds Ratio
Odds
One Class Classification
One Class Gaussian
One Hot Vector
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Optimizers
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orthonormal-vector
Overcomplete Autoencoder
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p-value
Padding in CNN
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Problem Solving Algorithm Selection
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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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Root Mean Squared Logarithmic Error (RMSLE)
ROUGE-L Score
ROUGE-LSUM Score
ROUGE-N Score
Saddle Points
scalar
Second Order Derivative or Hessian Matrix
Self Attention vs. Cross Attention
Self-Supervised Learning
Semi-supervised Learning
Sensitivity
SentencePiece Tokenization
Sequence-to-Sequence Model
Sigmoid Function
Sigmoid Kernel
Simple Linear Regression
Singular Value Decomposition (SVD)
Skip Gram Model
SMOTE
Soft Margin in SVM
Softmax
Softplus
Softsign
Some Common Behavioral Questions
Sources of Uncertainty
spacy-doc-object
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
Standardization
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
Root Mean Squared Logarithmic Error (RMSLE)
#machine-learning
#deep-learning
#evaluation
#interview
Root Mean Squared Logarithmic Error (RMSLE)
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2
Pros:
Good at
Handling Outliers
Cons:
Biased penalty
Penalizes underestimation more than the overestimation
Related Notes
F-Beta Score
Bias & Variance
R-squared Value
Confusion Matrix
Perplexity