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Advanced NLP with Scipy

Deep Learning by Ian Goodfellow

DS & Algo Interview

How To 100M Learning Text Video

How to Read a Paper

How To Write a Paper

ML Interview

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COIN

Large Language Models are Zero-Shot Rankers for Recommender Systems

MM-LLMs

Molmo and PixMo

MultiVENT

OpenPI-C

Semantic Product Search for Matching Structured Product Catalogs in E-Commerce

What is More Likely to Happen Next

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Zettelkasten

3 key question in data visualization

Accuracy

Activation Function

Active Learning

AdaBoost vs. Gradient Boosting vs. XGBoost

Adaboost

AdaDelta

AdaGrad

Adam

ADASYN

Adjusted R-squared Value

Alternative Hypothesis

Amazon Leadership Principles

Ancestral Sampling

Area Under Precision Recall Curve (AUPRC)

Attention

AUC Score

Autoencoder for Denoising Images

Autoencoder

Averaging in Ensemble Learning

Back Propagation

Backward Feature Elimination

Bag of Words

Bagging

Batch Normalization

Bayes Theorem

Bayesian Optimization Hyperparameter Finding

Beam Search

Behavioral Interview

BERT Embeddings

BERT

Bias & Variance

Bidirectional RNN or LSTM

Binary Cross Entropy

Binning or Bucketing

Binomial Distribution

bisect_left vs. bisect_right

BLEU Score

Boosting

Box Plot

Byte Level BPE

Byte Pair Encoding (BPE)

Causal Language Modeling

Causality vs. Correlation

Central Limit Theorem

Chain Rule

Challenges of NLP

Character Tokenizer

CNN

Co-occurrence based Word Embeddings

Co-Variance

Collinearity

Combination

Conditional Probability

conditionally-independent-joint-distribution

Confusion Matrix

Connections - Log Likelihood, Cross Entropy, KL Divergence, Logistic Regression, and Neural Networks

Contextualized Word Embeddings

Continuous Bag of Words

Continuous Random Variable

Contrastive Learning

Contrastive Loss

Convex vs Nonconvex Function

Cosine Similarity

Count based Word Embeddings

Cross Entropy

Cross Validation

Crossed Feature

Curse of Dimensionality

Data Augmentation

Data Imputation

Data Monitoring (DVC)

data visualization

DBScan Clustering

Debugging Deep Learning

Decision Boundary

Decision Tree (Classification)

Decision Tree (Regression)

Decision Tree

Decoder Only Transformer

Decoding Strategies

Density Sparse Data

Dependent Variable

Derivative

determinant

diagonal-matrix

Differentiation of Product

Differentiation

Digit Dp

Dimensionality Reduction

Discrete Random Variable

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

Encoder Only Transformer

Ensemble Learning

Entropy and Information Gain

Entropy

Essential Visualizations

Estimated Mean

Estimated Standard Deviation

Estimated Variance

Euclidian Distance

Euclidian Norm

Exhaustive Search

Expected Value for Continuous Events

Expected Value for Discrete Events

Expected Value

Exploding Gradient

Exponential Distribution

Extrinsic Evaluation

F-Beta Score

F-Beta@K

F1 Score

False Negative Error

False Positive Rate

FastText Embedding

Feature Engineering

Feature Extraction

Feature Hashing

Feature Preprocessing

Feature Selection

Finding Co-relation between two data or distribution

Fine Tuning Large Language Models

Forward Feature Selection

Foundation Model

frobenius-norm

fully-independent-join-distribution

fully-joint-joint-distribution

Gaussian Distribution

GBM

Generalized Discriminant Analysis (GDA)

Genetic Algorithm Hyperparameter Finding

Gini Impurity

Global Minima

GloVe Embedding

Gradient Boost (Classification)

Gradient Boost (Regression)

Gradient Boosting

Gradient Clipping

Gradient Descent

Gradient

Graph Convolutional Network (GCN)

Greedy Decoding

Grid Search Hyperparameter Finding

Group Normalization

GRU

Gumbel Softmax

Handling Imbalanced Dataset

Handling Missing Data

Handling Outliers

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

Hypothesis Testing

identity-matrix

Independent Component Analysis (ICA)

Independent Variable

InfoNCE Loss

Instructional Websites

Integration by Parts or Integration of Product

Internal Covariate Shift

Interquartile Range (IQR)

Interview Scheduling

Interview

Intrinsic Evaluation

Jaccard Distance

Jaccard Similarity

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

L2 or Ridge Regression

Label Encoding

Layer Normalization

Leaky ReLU

Learning Rate Scheduler

Lemmatization

LightGBM

Likelihood

Line Equation

Linear Discriminant Analysis (LDA)

Linear Regression

Local Minima

Log (Odds Ratio)

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

ML System Design

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

Permutation

Perplexity

Plots Compared

Polynomial Kernel

Polynomial Regression

Pooling

Population

Posterior Probability

Precision Recall Curve (PRC)

Precision

Precision@K

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

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Undersampling

#machine-learning #interview

In undersampling, we remove random amount of data from major ones to be similarly equal to the minor one.

Related Notes

Local Minima
Accuracy
AdaBoost vs. Gradient Boosting vs. XGBoost
Data Imputation
Standardization

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Recent Notes

interview

machine-learning

Handling Imbalanced Dataset