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One Class Classification
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Reno Talk @UMBC on Scale-2024
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Self Attention vs. Cross Attention
Self-Supervised Learning
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SentencePiece Tokenization
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Singular Value Decomposition (SVD)
Skip Gram Model
SMOTE
Soft Margin in SVM
Softmax
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Some Common Behavioral Questions
Sources of Uncertainty
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Specificity
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Stop Words
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Sub-word Tokenizer
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Support Vector Machine (SVM)
Support Vector
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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
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Word2Vec Embedding
WordPiece Tokenization
XGBoost
Z-score
Accuracy
#machine-learning
#interview
##evaluation
Accuracy
Accuracy is the percentage of how much data is correctly predicted
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Accuracy is a bad measure if the data is highly imbalanced.
Related Notes
Mean Absolute Error (MAE)
Undersampling
Gini Impurity
Area Under Precision Recall Curve (AUPRC)
Estimated Standard Deviation