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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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3 key question in data visualization
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Challenges of NLP
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conditionally-independent-joint-distribution
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Connections - Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks
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Cross Entropy
Cross Validation
Crossed Feature
Curse of Dimensionality
Data Augmentation
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Data Monitoring (DVC)
data visualization
DBScan Clustering
Debugging Deep Learning
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Dependent Variable
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diagonal-matrix
Differentiation of Product
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Dimensionality Reduction
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Discriminative vs. Generative Models
doing-literature-review
Domain vs. Codomain vs. Range
Dropout
Dying ReLU
Dynamic Programming (DP) in python
Eigendecomposition
eigenvalue-eigenvector
Elastic Net Regression
ELMo Embeddings
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FastText Embedding
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Finding Co-relation between two data or distribution
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Group Normalization
GRU
Gumbel Softmax
Handling Imbalanced Dataset
Handling Missing Data
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Heapq (nlargest or nsmalles)
Hierarchical Clustering
Hierarchical Softmax
Hinge Loss
Histogram
Homonym or Polysemy
How to Choose Kernel in SVM
How to combine in Ensemble Learning
How to prepare for Behavioral Interview
Huber Loss
Hyperparameters
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identity-matrix
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Independent Variable
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Instructional Websites
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Interquartile Range (IQR)
Interview Scheduling
Interview
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Jaccard Distance
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joint-distribuition
jupyter-notebook-on-server
K Fold Cross Validation
K-means Clustering
K-means vs. Hierarchical
K-nearest Neighbor (KNN)
Kernel in SVM
Kernel Regression
Kernel Trick
KL Divergence
L1 or Lasso Regression
L1 vs. L2 Regression
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Label Encoding
Layer Normalization
Leaky ReLU
Learning Rate Scheduler
Lemmatization
LightGBM
Likelihood
Line Equation
Linear Discriminant Analysis (LDA)
Linear Regression
Local Minima
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Log (Odds)
Log Scale
Log-cosh Loss
logarithm
Logistic Regression vs. Neural Network
Logistic Regression
Loss vs. Cost
lp-norm
LSTM
Machine Learning Algorithm Selection
Machine Learning vs. Deep Learning
Majority vote in Ensemble Learning
Manhattan Distance
Margin in SVM
Marginal Probability
Masked Language Modeling
matplotlib functions
matplotlib legend
Matrices
Matrix Factorization
max-norm
Maximal Margin Classifier
Maximum Likelihood
Mean Absolute Error (MAE)
Mean Absolute Percentage Error (MAPE)
Mean Squared Error (MSE)
Mean Squared Logarithmic Error (MSLE)
Mean
Median
Merge K-sorted List
Merge Overlapping Intervals
Meteor Score
Min Max Normalization
Mini Batch SGD
ML Case Study or ML Design
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Mode
Model Based vs. Instance Based Learning
MRR
Multi Class Cross Entropy
Multi Label Cross Entropy
Multi Layer Perceptron
Multicollinearity
Multiset
Multivariable Linear Regression
Multivariate Linear Regression
Multivariate Normal Distribution
Mutual Information
N-gram Method
Naive Bayes
Negative Log Likelihood
Negative Sampling
Nesterov Accelerated Gradient (NAG)
Neural Network Normalization
Neural Network
Next Sentence Prediction
norm
Normal Distribution
Normalization
Null Hypothesis
Odds Ratio
Odds
One Class Classification
One Class Gaussian
One Hot Vector
One vs One Multi Class Classification
One vs Rest or One vs All Multi Class Classification
Optimizers
orthogonal-matrix
orthonormal-vector
Overcomplete Autoencoder
Overfitting
Oversampling
p-value
Padding in CNN
Parameter vs. Hyperparameter
PCA vs. Autoencoder
Pearson Correlation
Perceptron
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Plots Compared
Polynomial Kernel
Polynomial Regression
Pooling
Population
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Precision Recall Curve (PRC)
Precision
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Principal Component Analysis (PCA)
Prior Probability
Probability Density Function
Probability Distribution
Probability Mass Function
Probability vs. Likelihood
Problem Solving Algorithm Selection
Proximal Policy Optimization (PPO)
Pruning in Decision Tree
PyTorch Loss Functions
PyTorch Refresher
Questions to ask in a Interview?
Quintile or Percentile
Quotient Rule or Differentiation of Division
R-squared Value
Radial Basis Kernel
Random Forest
Random Variable
Recall
Recall@K
Recommender System (RecSys)
Regularization
Reinforcement Learning from Human Feedback (RLHF)
Reinforcement Learning
Relational GCN
ReLU
Reno Talk @UMBC on Scale-2024
Retrieval Metrics
RMSProp
RNN
ROC Curve
Root Mean Squared Error (RMSE)
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
spacy-matcher
spacy-named-entities
spacy-operator-quantifier
spacy-pattern
spacy-pipeline
spacy-pos
spacy-semantic-similarity
spacy-syntactic-dependency
Specificity
Splitting tree in Decision Tree
Stacking or Meta Model in Ensemble Learning
Standard deviation
Standardization or Normalization
Standardization
Statistical Power
Statistical Significance
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
ReLU
#machine-learning
#interview
#activation-function
ReLU = Rectified Linear Unit
It solves the
Vanishing Gradient
problem
But introduces
Dying ReLU
problem
Most popular function for
CNN
or even
Neural Network
models
ReLU Formula
f
(
x
)
=
m
a
x
(
0
,
x
)
Derivative of ReLU
f
′
(
x
)
=
0
if x <= 0
f
′
(
x
)
=
1
if x > 0
Related Notes
PCA vs. Autoencoder
Oversampling
Supervised Learning
Maximal Margin Classifier
Linear Regression