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One Class Classification
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Optimizers
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p-value
Padding in CNN
Parameter vs. Hyperparameter
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Pearson Correlation
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Plots Compared
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Precision
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Probability vs. Likelihood
Problem Solving Algorithm Selection
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PyTorch Loss Functions
Questions to ask in a Interview?
Quintile or Percentile
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scalar
Second Order Derivative or Hessian Matrix
Self Attention vs. Cross Attention
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SentencePiece Tokenization
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Simple Linear Regression
Singular Value Decomposition (SVD)
Skip Gram Model
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Some Common Behavioral Questions
Sources of Uncertainty
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Specificity
Splitting tree in Decision Tree
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Standard deviation
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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
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Sub-word Tokenizer
Supervised Learning
Support Vector Machine (SVM)
Support Vector
Surprise
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Swallow vs. Deep Learning
t-SNE
Tanh
Text Preprocessing
TF-IDF
Three Way Partioning
Time Complexity of ML Models
Tokenizer
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
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
F1 Score
Precision
Recall
Sensitivity