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3 key question in data visualization

Accuracy

Activation Function

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AdaBoost vs. Gradient Boosting vs. XGBoost

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AdaGrad

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ADASYN

Adjusted R-squared Value

Alternative Hypothesis

Amazon Leadership Principles

Ancestral Sampling

Area Under Precision Recall Curve (AUPRC)

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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

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Binary Cross Entropy

Binning or Bucketing

Binomial Distribution

bisect_left vs. bisect_right

BLEU Score

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Box Plot

Byte Level BPE

Byte Pair Encoding (BPE)

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

Curse of Dimensionality

Data Augmentation

Data Imputation

Data Monitoring (DVC)

Data Normalization

data visualization

DBScan Clustering

Debugging Deep Learning

Decision Boundary

Decision Tree (Classification)

Decision Tree (Regression)

Decision Tree

Decoding Strategies

Density Sparse Data

Dependent Variable

Derivative

determinant

diagonal-matrix

Differentiation of Product

Differentiation

Digit Dp

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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

Ensemble Learning

Entropy and Information Gain

Entropy

Essential Visualizations

Estimated Mean

Estimated Standard Deviation

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Euclidian Distance

Euclidian Norm

Exhaustive Search

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Exponential Distribution

Extrinsic Evaluation

F-Beta Score

F1 Score

False Negative Error

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FastText Embedding

Feature Engineering

Feature Extraction

Feature Preprocessing

Feature Selection

Finding Co-relation between two data or distribution

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

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Gradient Descent

Gradient

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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

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 System Design

Mode

Model Based vs. Instance Based Learning

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

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

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

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

Recommender System (RecSys)

Regularization

Reinforcement Learning from Human Feedback (RLHF)

Reinforcement Learning

Relational GCN

ReLU

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

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

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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 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

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ML Interview

#interview #machine-learning #deep-learning #nlp #vision

#math
#statistics
- Histogram
- Distribution ⭐️
  - Uniform Distribution
  - Normal Distribution
  - Multivariate Normal Distribution
  - Multinomial Normal Distribution
  - Gaussian Distribution
  - Exponential Distribution
  - Binomial Distribution
  - Poisson Distribution
  - Population
- Mean
- Mode
- Median
- Variance
- Standard deviation
- Co-Variance
- Finding Co-relation between two data or distribution
  - Pearson Correlation
  - R-squared Value
  - Mutual Information
  - Cosine Similarity ⭐️
  - Co-Variance
  - Jaccard index
  - Chi Squared Test
- Distance Metric
  - Manhattan Distance
  - Euclidian Distance
  - Cosine Similarity
  - Mahalanobis Distance
  - Hamming Distance
  - Chebychev Distance
- Hypothesis Testing
- Null Hypothesis
- Statistical Test
- p-value
- Odds
  - Log (Odds)
- Central Limit Theorem
- Quintile or Percentile
- Log Scale
#probability
#visualization
- Plots Compared
#machine-learning
- Supervised Learning
  - Linear Regression ⭐️
  - Polynomial Regression
  - Bayesian Regression
  - Logistic Regression ⭐️
    - Multinomial Logistic Regression
  - Perceptron ⭐️
  - Multi Layer Perceptron ⭐️
  - GLM
  - LDA
  - UMAP
  - t-SNE
  - Support Vector Machine (SVM) ⭐️
    - SVR ⭐️
    - SVC
    - Kernel in SVM
  - K-nearest Neighbor (KNN)
  - Decision Tree ⭐️
    - GBM
    - Adaboost
    - XGBoost
    - LightGBM
    - CatBoost
    - Pruning in Decision Tree
  - Ensemble Learning
    - Bagging
      - Random Forest ⭐️
    - Boosting
      - Gradient Boosting ⭐️
    - How to combine in Ensemble Learning
  - Naive Bayes
    - Gaussian ⭐️
    - Multinomial ⭐️
    - Bernouli
    - Complement
    - Categorical
  - Markov Chain
- Unsupervised Learning
  - Clustering
    - K-means Clustering ⭐️
    - Hierarchical Clustering
    - DBScan Clustering
    - HDBScan Clustering
    - K-means vs. Hierarchical
    - Spectral Clustering
    - Gaussian Mixture Model
  - Dimensionality Reduction
    - Principal Component Analysis (PCA) ⭐️
    - UMAP
    - HeatMap
    - t-SNE plots
    - Autoencoder
  - Association
    - Apriori
  - Expectation Minimization
- Semi-supervised Learning
- Recommendation
  - Content Filtering ⭐️
  - Collaborative Filtering ⭐️
- Metric Learning
- Learning to Rank
  - Pointwise Learning to Rank
  - Pairwise Learning to Rank
  - Listwise Learning to Rank
- Probabilistic Graphical Model
  - Conditional Random Field
  - Bayessian Network
#deep-learning
- CNN
- RNN ⭐️
- LSTM ⭐️
- Bidirectional RNN or LSTM ⭐️
- GRU ⭐️
- Autoencoder
  - Standard ⭐️
  - Variational Autoencoder ⭐️
  - PCA vs. Autoencoder
  - Overcomplete Autoencoder
  - Undercomplete Autoencoder
  - Uses: ⭐️
    - Autoencoder for Anomaly Detection
    - Autoencoder for Denoising Images
    - Representation Learning
- Attention Reference
  - Self Attention ⭐️
  - Masked Self Attention ⭐️
  - Multihead Self Attention ⭐️
  - Encoder-Decoder Attention
  - Factorized Self Attention
  - Flash Attention
  - Cross Attention
- Transformer
  - Encoder-decoder ⭐️
  - Encoder Only ⭐️
  - Decoder Only ⭐️
- Contrastive Learning ⭐️
- Graph Convolutional Network (GCN) ⭐️
  - Relational GCN
  - Graph Attention Network
- Word Embeddings
  - TF-IDF ⭐️
  - Word2Vec ⭐️ Ref
    - Continuous Bag of Words (CBOW)
    - Skip Gram Model
  - FastText ⭐️
  - Glove ⭐️
  - Elmo
  - BERT Embeddings
- Activation Function
  - Sigmoid Function ⭐️
  - Tanh ⭐️
  - Softplus
  - Softsign
  - Softmax ⭐️
  - ReLU ⭐️
  - Leaky ReLU
  - PReLU
  - ELU
  - SELU
  - Swiss ReLU
  - GeLU
- Optimizers
  - Gradient Descent ⭐️
  - Stochastic Gradient Descent (SGD) ⭐️
  - Mini Batch SGD ⭐️
  - Stochastic Gradient Descent with Momentum
  - Nesterov Accelerated Gradient (NAG)
  - Adaptive Methods
    - AdaGrad
    - AdaDelta
    - RMSProp
    - Adam
    - Adamax
    - AMSGrad
    - NADAM
- Generative Adversarial Network
- Genetic Algorithms
- Reinforcement Learning
#loss-in-ml
- Entropy ⭐️
- Cross Entropy
  - Multi Class Cross Entropy ⭐️
  - Multi Label Cross Entropy ⭐️
- KL Divergence ⭐️
- Contrastive Loss ⭐️
- Triplet Loss ⭐️
- InfoNCE Loss ⭐️
- Mean Squared Error (MSE) ⭐️
- Mean Absolute Error (MAE)
- Mean Squared Logarithmic Error (MSLE)
- Mean Absolute Percentage Error (MAPE)
- Huber Loss
- Log-cosh Loss
- Poisson Loss
- Hinge Loss
#evaluation
- Extrinsic Evaluation
- Intrinsic Evaluation
- Perplexity ⭐️
- Precision
- Recall
- Accuracy
- F1 Score ⭐️
- Sensitivity ⭐️
- Specificity ⭐️
- True Positive Rate
- False Positive Rate
- Confusion Matrix ⭐️
- Bias & Variance ⭐️
- AUC Score
- ROC Curve
- BLEU Score ⭐️
- ROUGE-N Score ⭐️
- ROUGE-L Score ⭐️
- Meteor Score
- BERTScore
- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
- R-squared Value
- Root Mean Squared Logarithmic Error (RMSLE)
Regularization
- L1 or Lasso Regression ⭐️
- L2 or Ridge Regression ⭐️
- Elastic Net Regression
- Dropout ⭐️
Misc.
- Machine Learning vs. Deep Learning
- Cross Validation
- Multi Class Classification
  - One vs Rest or One vs All Multi Class Classification
  - One vs One Multi Class Classification
- Internal Covariate Shift
- Discriminative vs. Generative Models
- Kernel Regression
- One Class Classification
  - One Class Gaussian
  - One Class K-means
  - One Class KNN
  - One Class SVM
- Gumble Softmax ⭐️
- Normalization
  - Data Normalization
  - Batch Normalization
  - Layer Normalization
- Generation
  - Greedy Decoding ⭐️
  - Beam Search ⭐️
  - Random Sampling ⭐️
  - Minimum Bayes Risk
- Handling Missing Data ⭐️
- Overfitting ⭐️
- Handling Imbalanced Dataset ⭐️
  - SMOTE
  - ADASYN
- Handling Outliers ⭐️
- Tokenizer
  - Byte Pair Encoding (BPE) ⭐️
  - WordPiece Tokenization
  - SentencePiece Tokenization
- Parametric vs Non Parametric ⭐️
- Model Based vs. Instance Based Learning ⭐️
- Swallow vs. Deep Learning ⭐️
- Parameter vs. Hyperparameter ⭐️
- Exploding Gradient ⭐️
- Vanishing Gradient ⭐️
- Hyperparameters
- Loss vs. Cost
- Gradient Clipping
- Grad accumulation
- Stemming
- Lemmatization
- Causality vs. Correlation
- Negative Sampling
- Data Augmentation
- Data Imputation
- Hinge Loss
- Feature Selection
- Framenet
- Wordnet
- Verbnet
- AMR Graph
- Transfer Learning
- Teacher Forcing ⭐️
- Student Forcing ⭐️
- Curriculum Learning ⭐️
- Weight Initialization
  - Xavier
  - Normal
- Learning Rate Scheduler ⭐️
- Fine Tuning Speedup
  - LORA ⭐️
  - Adapter ⭐️
- Hyper parameter finding
  - Grid Search Hyperparameter Finding
  - Random Search
  - Bayesian Optimization Hyperparameter Finding
  - Genetic Algorithm Hyperparameter Finding
  - Gradient based techniques
- Different types of Learning
  - Zero Shot Learning
  - One Shot Learning
  - Few Shot Learning
  - Transfer Learning
  - Active Learning
- Idea about SOTA Research
  - LLaMA
  - ChatGPT
  - BERT Ref
  - BART
  - GPT GPT-2
  - ROBERTA
  - ALbert
  - XLNET
  - Electra
  - DistilBert
  - [ ]
- ELBO
- End to End Machine Learning Pipeline
- Convex vs. Non-Convex
- Convex vs. Non-Convex Optimization
- One Hot Vector
- LabelEncoding
- One Hot Encoding vs. Label Encoding
- Inductive Bias
- Selection Bias
- Type 1 Error vs. Type 2 Error

Related Notes

Forward Feature Selection
One Class Classification
Soft Margin in SVM
Singular Value Decomposition (SVD)
Stride in CNN

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