Source abbreviations:
**AJE:** Algorithms
**BA:** Bandit Algorithms
**BJP:** (me)
**CI:** Collective Intelligence
**CO:** Convex Estimation
**DIDL:** Dive into Deep Learning
**DLG:** Deep Learning (Goodfellow, et al)
**DMMD:** Data Mining of Massive Datasets
**DSA:** Data structures & Algorithms
**DSCL:** Data Science at the Command Line
**EA:** Elementary Algorithms
**ESL:** Elements of Statistical Learning
**FDS:** Foundations of Data Science
**GT:** Geometric Topology
**ITA:** Intro to Algorithms
**JE:** Algorithms
**NP:** Numeric Python
**SKL:** Scikit-learn
**SM:** ML cheatsheet
**RL:** Reinforcement Learning

Tags:

(multiple) approximations arithmetic association rules autoencoders bandit algorithms bash bayes cheatsheets classification clustering combinationals computation - complexity - performance - benchmarking data structures datasets deep learning architectures density estimation design dimensional reduction dynamic programming ensembles evaluation feature engineering file I/O gaussians generative models geometry graphs greedy algos inference information theory interviewing kernels label spreading, label propagation latent variables learning linear models linear programming make markov chains matrix math max likelihood estimation (MLE) methods mixtures monte carlo multilabel natural language processing novelties-outliers numerical analysis numpy pandas parametric models performance planning planning / capacity probabilistic analysis probability & statistics pycaret recommenders recurrent NNs recursion regression reinforcement learning restricted boltzmann machines robotics searching & sorting set theory streams strings survival analysis svd svms sympy tbd tensorflow time series tools topology training use cases vision visualization wavelets

(multiple) approximations arithmetic association rules autoencoders bandit algorithms bash bayes cheatsheets classification clustering combinationals computation - complexity - performance - benchmarking data structures datasets deep learning architectures density estimation design dimensional reduction dynamic programming ensembles evaluation feature engineering file I/O gaussians generative models geometry graphs greedy algos inference information theory interviewing kernels label spreading, label propagation latent variables learning linear models linear programming make markov chains matrix math max likelihood estimation (MLE) methods mixtures monte carlo multilabel natural language processing novelties-outliers numerical analysis numpy pandas parametric models performance planning planning / capacity probabilistic analysis probability & statistics pycaret recommenders recurrent NNs recursion regression reinforcement learning restricted boltzmann machines robotics searching & sorting set theory streams strings survival analysis svd svms sympy tbd tensorflow time series tools topology training use cases vision visualization wavelets

(multiple)

data science cheatsheet 2.0 (aaron wang)

other topics (FDS)

distributions; hypothesis testing; concepts; model evaluation; linear regression; logistic regression; decision trees; naive bayes; svms; knns; clustering; dimensional reduction (PCA, LDA, FA); NLP; neural nets (basics, CNNs, RNNs); boosting; recommenders; reinforcement learning; anomoly detection

other topics (FDS)

ranking & social choice; compressed sensing & sparse vectors; use cases; an uncertainty principle; gradients; linear programming; integer optimization; semi-definite programming

approximations

approximate-inference (DLG)

approximations (algorithm reductions) (ADM)

approximations (algorithm reductions) (ITA)

inference as optimization

expectation maximization (EM)

MAP inference | sparse coding

variational inference

learned approx inference

expectation maximization (EM)

MAP inference | sparse coding

variational inference

learned approx inference

approximations (algorithm reductions) (ADM)

algo reductions

basic hardness reductions

satisfiability

creative reductions

"proving" hardness

P vs NP hardness

NP-complete problems

basic hardness reductions

satisfiability

creative reductions

"proving" hardness

P vs NP hardness

NP-complete problems

approximations (algorithm reductions) (ITA)

the vertex-cover problem

the traveling salesman problem

the set-cover problem

randomization & linear programming

the subset-sum problem

the traveling salesman problem

the set-cover problem

randomization & linear programming

the subset-sum problem

arithmetic

complex-numbers (LAY)

computation (DLG)

factoring primes (ADM)

linear algebra (LAY)

linear equation solvers (ADM)

number theory (ITA)

random numbers (ADM)

examples; geometric representation; powers; R^2

computation (DLG)

underflow, overflow

poor conditioning

gradient-based optimization

jacobian & hessian matrices

constrained optimization

linear least squares

poor conditioning

gradient-based optimization

jacobian & hessian matrices

constrained optimization

linear least squares

factoring primes (ADM)

is n a prime number? if not, what are its factors?

linear algebra (LAY)

linear equations

row reductions

vector equations

Ax=b

solution sets of linear systems

applications

linear independence

linear transforms

linear models - business, science, engineering

row reductions

vector equations

Ax=b

solution sets of linear systems

applications

linear independence

linear transforms

linear models - business, science, engineering

linear equation solvers (ADM)

if A = an mxm matrix, and b = an mx1 vector, what is vector X such that AX=b?

number theory (ITA)

basics (divisors, primes/composites)

greatest common divisor (Euclid)

modular math (group theory?)

linear equations

the chinese remainder problem

powers

RSA public-key crypto

prime testing

factorization (integer)

greatest common divisor (Euclid)

modular math (group theory?)

linear equations

the chinese remainder problem

powers

RSA public-key crypto

prime testing

factorization (integer)

random numbers (ADM)

(also part of "numericals" chapter of ADM.)

association rules

association rules | market basket analysis (ESL)

frequent itemsets (DMMD)

frequent itemsets (DMMD)

market-basket modeling; association rules; a-priori algorithm; large datasets & main memory; limited-pass algorithms; counting items in streams

autoencoders

autoencoders (DLG)

autoencoders with Tensorflow (HoML)

undercomplete AEs; regularized AEs; representational power, layer size & depth; stochastic encoders & decoders; denoising AEs; learning manifolds with AEs; predictive sparse decomposition; applications

autoencoders with Tensorflow (HoML)

bandit algorithms

UCB - asymptotic-optimality (BA)

UCB-algorithm-bernoulli-noise (BA)

UCB-algorithm-minimax-optimality (BA)

bandits-adversarial-vs-stochastic-linear (BA)

bandits-bayes (BA)

bandits-combinatorial (BA)

bandits-concentration-of-measure- (BA)

bandwidth-reduction (ADM)

basis-expansions-regularization (ESL)

bayes-empirical-estimation (CSI)

contextual (BA)

convex-analysis (BA)

exp3 (BA)

exp3-IX (BA)

exp3-adversarial-linear (BA)

explore-then-commit (BA)

follow-the-leader-mirror-descent (BA)

index (BA)

info theory (BA)

intro (BA)

least-squares-estimators-confidence-bounds (BA)

least-squares-estimators-optimal-design (BA)

lower-bounds (BA)

lower-bounds-high-probability (BA)

lower-bounds-instance-dependent (BA)

lower-bounds-minimax (BA)

markov-decisions (BA)

non-stationary (BA)

partial-monitoring (BA)

probability (BA)

pure-exploration (BA)

ranking (BA)

stochastic-finite (BA)

stochastic-linear (BA)

stochastic-linear-asymptotic-lower-bounds (BA)

stochastic-linear-finite-many-arms (BA)

stochastic-linear-minimax-lower-bounds (BA)

stochastic-linear-sparsity (BA)

stochastic-markov (BA)

thompson-sampling (BA)

upper-confidence-bound (BA)

UCB-algorithm-bernoulli-noise (BA)

UCB-algorithm-minimax-optimality (BA)

bandits-adversarial-vs-stochastic-linear (BA)

bandits-bayes (BA)

bandits-combinatorial (BA)

bandits-concentration-of-measure- (BA)

bandwidth-reduction (ADM)

basis-expansions-regularization (ESL)

bayes-empirical-estimation (CSI)

contextual (BA)

convex-analysis (BA)

exp3 (BA)

exp3-IX (BA)

exp3-adversarial-linear (BA)

explore-then-commit (BA)

follow-the-leader-mirror-descent (BA)

index (BA)

info theory (BA)

intro (BA)

least-squares-estimators-confidence-bounds (BA)

least-squares-estimators-optimal-design (BA)

lower-bounds (BA)

lower-bounds-high-probability (BA)

lower-bounds-instance-dependent (BA)

lower-bounds-minimax (BA)

markov-decisions (BA)

non-stationary (BA)

partial-monitoring (BA)

probability (BA)

pure-exploration (BA)

ranking (BA)

stochastic-finite (BA)

stochastic-linear (BA)

stochastic-linear-asymptotic-lower-bounds (BA)

stochastic-linear-finite-many-arms (BA)

stochastic-linear-minimax-lower-bounds (BA)

stochastic-linear-sparsity (BA)

stochastic-markov (BA)

thompson-sampling (BA)

upper-confidence-bound (BA)

bash

common linux/bash commands (Data Science - Command Line)

environment (alias, bash, cols, for, sudo, ...)

files & directories (body, cd, cat, chmod, ...)

pattern matching (awk, sed, grep)

deployment (aws, git, )

CSV data

JSON data

online data (curl, scp, scrape, ssh)

integer/date sequences,br> file extraction/compression (tar, tree, uniq, ...)

files & directories (body, cd, cat, chmod, ...)

pattern matching (awk, sed, grep)

deployment (aws, git, )

CSV data

JSON data

online data (curl, scp, scrape, ssh)

integer/date sequences,br> file extraction/compression (tar, tree, uniq, ...)

bayes

bayes inference (CSI)

bayes nets (directed graphs) (SM)

bayes statistics (NP)

bayesian statistics (SM)

two examples

uninformed prior distributions

flaws in frequentist inference

bayes vs frequentist comparison

uninformed prior distributions

flaws in frequentist inference

bayes vs frequentist comparison

bayes nets (directed graphs) (SM)

bayes statistics (NP)

intro & model definition

sampling posterior distributions

linear regression

sampling posterior distributions

linear regression

bayesian statistics (SM)

intro

posterior distribution

MAP estimates

bayes model selection

priors

hierarchical bayes

empirical bayes

decision theory

posterior distribution

MAP estimates

bayes model selection

priors

hierarchical bayes

empirical bayes

decision theory

cheatsheets

deep learning cheatsheet (2018) (SCDL)

sampling methods (PSC)

CNNs, RNNs, tips & tricks

sampling methods (PSC)

inverse transform sampling; the bootstrap; rejection sampling; importance sampling

classification

cal housing market analysis (HoML)

classification basics (HoML)

discriminants (LDA, QDA) (SKL)

linear classification (ESL)

logistic regression (SKL)

metrics (SKL)

multiclass & multioutput algos (SKL)

multilayer perceptron (MLP) (SKL)

naive bayes (SKL)

nearest neighbors (SKL)

nearest neighbors (ESL)

classification basics (HoML)

MNIST, aka hello world

confusion matrix

metrics (precision,recall)

ROC curve

multiclass classification

multilabel classification

multioutput classification

confusion matrix

metrics (precision,recall)

ROC curve

multiclass classification

multilabel classification

multioutput classification

discriminants (LDA, QDA) (SKL)

Linear DA

Quadratic DA

Quadratic DA

linear classification (ESL)

regression - indicator matrix

linear discriminant analysis (LDA)

logistic regression

hyperplanes

linear discriminant analysis (LDA)

logistic regression

hyperplanes

logistic regression (SKL)

solvers - liblinear, newton-cg, lbfgs, sag, saga

metrics (SKL)

accuracy, top-K accuracy, balanced accuracy

cohen's kappa, confusion matrix, classification report

hamming loss, precision, recall, f-measure

precision-recall curve, avg precision

precision-recall curve (multilabel)

jaccard similarity

hinge loss

log loss

matthews correleation coefficient

confusion matrix (multilabel)

ROC curve

detection-error tradeoff (DET)

zero-one loss

brier score

cohen's kappa, confusion matrix, classification report

hamming loss, precision, recall, f-measure

precision-recall curve, avg precision

precision-recall curve (multilabel)

jaccard similarity

hinge loss

log loss

matthews correleation coefficient

confusion matrix (multilabel)

ROC curve

detection-error tradeoff (DET)

zero-one loss

brier score

multiclass & multioutput algos (SKL)

intro

multiclass (aka label binarization)

one-vs-rest

multilabel

one-vs-one

output code

multioutput

classifier chains

multiclass-multioutput (aka multitask)

multiclass (aka label binarization)

one-vs-rest

multilabel

one-vs-one

output code

multioutput

classifier chains

multiclass-multioutput (aka multitask)

multilayer perceptron (MLP) (SKL)

naive bayes (SKL)

NB classification (gaussian, multinomial, complement, bernoulli)

categorical NB

categorical NB

nearest neighbors (SKL)

basic algos (ball tree, KD tree, ...)

KNNs & radius-based algos

nearest centroids

neighborhood components analysis (NCA)

KNNs & radius-based algos

nearest centroids

neighborhood components analysis (NCA)

nearest neighbors (ESL)

prototype methods (kmeans, learning vector quant, gaussian mixtures)

knn classifiers

adaptive NN methods

computational performance

knn classifiers

adaptive NN methods

computational performance

clustering

biclustering methods (SKL)

clustering (DMMD)

clustering (FDS)

clustering (ESL)

clustering methods (SKL)

clustering metrics (SKL)

intro, spectral co/biclustering

clustering (DMMD)

intro (data, strategies, dimensionality)

hierarchical

k-means

CURE (clustering using representatives)

non-euclidean spaces

clustering for streams & parallelism

hierarchical

k-means

CURE (clustering using representatives)

non-euclidean spaces

clustering for streams & parallelism

clustering (FDS)

intro

k-means (lloyds algo, wards algo)

k-center

low-error

spectral

approximation stability

high-density

kernel methods

recursive clustering w/ sparse cuts

dense submatrices & communities

community finding & graph partitions

spectral clustering & social nets

k-means (lloyds algo, wards algo)

k-center

low-error

spectral

approximation stability

high-density

kernel methods

recursive clustering w/ sparse cuts

dense submatrices & communities

community finding & graph partitions

spectral clustering & social nets

clustering (ESL)

clustering methods (SKL)

Kmeans & Kmeans minibatch

Affinity propagation

Mean shifts

Spectral clustering

Agglomerative clustering

Hierarchical clustering

DBSCAN

Birch

OPTICS

Affinity propagation

Mean shifts

Spectral clustering

Agglomerative clustering

Hierarchical clustering

DBSCAN

Birch

OPTICS

clustering metrics (SKL)

rand index; mutual info score; homogeneity / completeness / v-measure; Fowlkes-Mallows score; silhouette coefficient; Calinski-Harabasz index; Davies-Bouldin index

combinationals

job scheduling (ADM)

partitions (ADM)

permutations (ADM)

satisfiability (ADM)

given a directed acyclic graph (vertices = jobs, edges = task dependencies), what schedule completes the job in minimum time/effort?

partitions (ADM)

given integer n, generate partitions that add up to n.

permutations (ADM)

given n, generate a set of items of length n.

satisfiability (ADM)

given a set of logical constraints, is there a configuration that satisfies the set?

computation - complexity - performance - benchmarking

data structures

b-trees (ITA)

datastructs (ADM)

datastructs (DSA)

datastructs augmenting (ITA)

datastructs disjoint (ITA)

dictionaries (ADM)

dictionaries (DSA)

fibonacci heaps (ITA)

hashes (ITA)

heaps (EA)

intro (ITA)

kd trees (ADM)

lists (EA)

priority queues (ADM)

priority queues (DSA)

queues sequences (EA)

red black trees (ITA)

steiner trees (ADM)

suffix trees (ADM)

trees (EA)

van emde boas trees (ITA)

datastructs (ADM)

datastructs (DSA)

datastructs augmenting (ITA)

datastructs disjoint (ITA)

dictionaries (ADM)

dictionaries (DSA)

fibonacci heaps (ITA)

hashes (ITA)

heaps (EA)

intro (ITA)

kd trees (ADM)

lists (EA)

priority queues (ADM)

priority queues (DSA)

queues sequences (EA)

red black trees (ITA)

steiner trees (ADM)

suffix trees (ADM)

trees (EA)

van emde boas trees (ITA)

datasets

deep learning architectures

CNN cheatsheet (SCDL)

adversarial apps (paperswithcode)

convolutional NNs (DLG)

convolutionl NNs (DLG)

deep feedforward NNs (DLG)

deep generative models (DLG)

deep learning (DLG)

gans (DIDL)

intro (ESL)

intro to neural nets (CSI)

linear NNs (DIDL)

neural network zoo (asimov institute)

perceptrons (DIDL)

representation learning (DLG)

structured probabilistic models (DLG)

adversarial apps (paperswithcode)

convolutional NNs (DLG)

convolutionl NNs (DLG)

deep feedforward NNs (DLG)

deep generative models (DLG)

deep learning (DLG)

gans (DIDL)

intro (ESL)

intro to neural nets (CSI)

intro; fitting; autoencoders; deep learning; learning (dropout, input distortion)

linear NNs (DIDL)

neural network zoo (asimov institute)

perceptrons (DIDL)

representation learning (DLG)

greedy layer-wise unsupervised pretraining

transfer learning | domain adaptation

semi-supervised disentangling of causal factors

distributed representation

exponential gains from depth

providing clues to find underlying causes

transfer learning | domain adaptation

semi-supervised disentangling of causal factors

distributed representation

exponential gains from depth

providing clues to find underlying causes

structured probabilistic models (DLG)

challenges; using graphs; sampling from graphs; advantages; dependencies; infererence & approx inference

density estimation

density estimates (PSC)

density estimation methods (SKL)

density estimates

histograms

kernel density estimator (KDE)

histograms

kernel density estimator (KDE)

density estimation methods (SKL)

intro, histograms, kernel density estimates (KDE)

design

dimensional reduction

Nonlinear Dimension Reduction | Local Multidimensional Scaling (ESL)

component analysis & matrix factorization (SKL)

independent component analysis (ICA) (ESL)

manifold learning (SKL)

multi-dimensional scaling (MDS) (ESL)

non-negative matrix transform (NNMF) (ESL)

principal component analysis (PCA) ()

principal components (ESL)

self-organized maps (ESL)

component analysis & matrix factorization (SKL)

independent component analysis (ICA) (ESL)

manifold learning (SKL)

multi-dimensional scaling (MDS) (ESL)

non-negative matrix transform (NNMF) (ESL)

principal component analysis (PCA) ()

principal components (ESL)

self-organized maps (ESL)

dynamic programming

dynamic programming (ADM)

dynamic programming (ITA)

dynamic programming (JE)

dynamic programming (ITA)

dynamic programming (JE)

intro; faster fibonacci numbers; smart recursion; greed is stupid; longest increasing subsequence; edit distance; subset sum; binary search trees; dynamic programming on trees;

ensembles

bagging (SKL)

boosting (adaboost, gradient tree boosting, histogram boosting) (SKL)

boosting additive trees (ESL)

catboost (catboost.ai)

decision trees (HoML)

decision trees (SKL)

ensemble learning (HoML)

ensembles (ESL)

random forests (ESL)

random forests boosting (CSI)

stacking (general case) (SKL)

voting (SKL)

xgboost (xgboost.ai)

boosting (adaboost, gradient tree boosting, histogram boosting) (SKL)

boosting additive trees (ESL)

catboost (catboost.ai)

gradient boosting on decision trees

decision trees (HoML)

decision trees (SKL)

ensemble learning (HoML)

ensembles (ESL)

random forests (ESL)

random forests boosting (CSI)

stacking (general case) (SKL)

voting (SKL)

xgboost (xgboost.ai)

gradient boosting library

evaluation

feature engineering

composite transforms (SKL)

data aggregation & grouping (PDA)

data cleaning (PDA)

data imputation (SKL)

data preprocessing (SKL)

feature extraction (SKL)

feature extraction (text) (SKL)

image patch extraction (SKL)

join, combine, reshape (PDA)

pairwise-data operations (SKL)

prediction target transforms (SKL)

random projections (SKL)

scrubbing data (DSCL)

data aggregation & grouping (PDA)

data cleaning (PDA)

missing data; transforms; extension data types; string ops; category ops

data imputation (SKL)

data preprocessing (SKL)

feature extraction (SKL)

feature extraction (text) (SKL)

image patch extraction (SKL)

join, combine, reshape (PDA)

hierarchical indexing; combining datasets; reshaping; pivoting

pairwise-data operations (SKL)

prediction target transforms (SKL)

random projections (SKL)

scrubbing data (DSCL)

transforms

plain text

CSV

XML,HTML,JSON

plain text

CSV

XML,HTML,JSON

file I/O

data I/O (DSCL)

file I/O (NP)

file I/O - datatypes (PDA)

local data to docker

internet downloads (curl, ...)

decompressions (zip, ...)

excel to CSV

relational DBs

web APIs

authentication

streaming APIs

internet downloads (curl, ...)

decompressions (zip, ...)

excel to CSV

relational DBs

web APIs

authentication

streaming APIs

file I/O (NP)

CSV; HDF5; h5py; Pytables; serialization

file I/O - datatypes (PDA)

text files; JSON; XML/HTML scraping; binary data; web APIs; databases

gaussians

generative models

generative models - discrete data (SM)

generative classifiers; bayesian concept learning; beta-binomial model; dirichlet-multinomial model; naive bayes classifiers

geometry

bin packing (ADM)

convex hulls (ADM)

geometric primitives (ADM)

geometry (ITA)

intersections (ADM)

line arrangements (ADM)

medial axis xforms (ADM)

minkowski sum (ADM)

motion planning (ADM)

nearest neighbors (ADM)

point location (ADM)

polygon partitions (ADM)

polygon simplification (ADM)

range search (ADM)

shape similarity (ADM)

spatial structures (DSA)

triangulation (ADM)

vector spaces (LAY)

given n items and m bins - store all the items using the smallest number of bins.

convex hulls (ADM)

geometric primitives (ADM)

geometry (ITA)

intersections (ADM)

line arrangements (ADM)

medial axis xforms (ADM)

minkowski sum (ADM)

motion planning (ADM)

nearest neighbors (ADM)

point location (ADM)

polygon partitions (ADM)

polygon simplification (ADM)

range search (ADM)

shape similarity (ADM)

spatial structures (DSA)

multi-dimensional structures; planar straight-line graphs; search trees; quad/octal trees; binary space partitioning trees; r-trees; spatio-temporal data; kinetic structures; online dicts; cuttings; approximate geometric queries

triangulation (ADM)

vector spaces (LAY)

graphs

basic algorithms (JE)

chinese-postman (ADM)

cliques (ADM)

connected components (ADM)

edge coloring (ADM)

edge vertex connectivity (ADM)

feedback edge vertex set (ADM)

flows & cuts applications (JE)

graph algos (ITA)

graph algos (SOTA) (paperswithcode)

graph datastructs (ADM)

graph drawing (ADM)

graph generation (ADM)

graph isomorphism (ADM)

graph link analysis (DMMD)

graph partition (ADM)

graph traversal (ADM)

graphs connected components (ADM)

graphs hard (ADM)

graphs polynomial time (ADM)

graphs weighted (ADM)

graphviz (tool) (graphviz)

hamiltonian cycles (ADM)

matching (ADM)

maxflow (ITA)

min spanning trees (JE)

min spanning trees (ITA)

minimum spanning tree (ADM)

network flow (ADM)

planarity detection (ADM)

random graphs (FDS)

social graphs (DMMD)

sparse matrices graphs (NP)

transitive closure (ADM)

traveling salesman (ADM)

tree drawing (ADM)

undirected graphs (ESL)

vertex coloring (ADM)

vertex cover (ADM)

definitions; representations; data structures; whatever-first search; depth-first; breadth-first; best-first; disconnected graphs; directed graphs

reductions (flood fill)

reductions (flood fill)

chinese-postman (ADM)

given a graph, finding the shortest path touching each edge.

cliques (ADM)

how to find the largest clique (cluster) in a graph?

connected components (ADM)

find the pieces of a graph, where vertices x & y are members of different components if no path exists from x to y.

edge coloring (ADM)

what's the smallest set of colors needed to color the edges of a graph, such that no two same-color edges share a common vertex?

edge vertex connectivity (ADM)

what's the smallest subset of vertices (edges) whose deletion will disconnect a graph?

feedback edge vertex set (ADM)

flows & cuts applications (JE)

edge-disjoint paths

vertex capacities & vertex-disjoint paths

bipartite matching

tuple selection

disjoint-path covers

baseball elimination

project selection

vertex capacities & vertex-disjoint paths

bipartite matching

tuple selection

disjoint-path covers

baseball elimination

project selection

graph algos (ITA)

representations; breadth-first search; depth-first search; topological sorting; strongly-connected components;

graph algos (SOTA) (paperswithcode)

graph datastructs (ADM)

adjancency matrices; adjancency lists

graph drawing (ADM)

graph generation (ADM)

graph isomorphism (ADM)

given two graphs G & H, find a function from G's vertices to H's vertices such that G & H are identical.

graph link analysis (DMMD)

PageRank; link spam; hubs & authorities

graph partition (ADM)

given a weighted graph G and integers k & m, partition the vertices of G into m equally-sized subsets such that the total edge cost spanning the subsets is at most k.

graph traversal (ADM)

graphs connected components (ADM)

graphs hard (ADM)

graphs polynomial time (ADM)

graphs weighted (ADM)

graphviz (tool) (graphviz)

hamiltonian cycles (ADM)

matching (ADM)

maxflow (ITA)

min spanning trees (JE)

min spanning trees (ITA)

minimum spanning tree (ADM)

network flow (ADM)

planarity detection (ADM)

random graphs (FDS)

social graphs (DMMD)

sparse matrices graphs (NP)

transitive closure (ADM)

traveling salesman (ADM)

tree drawing (ADM)

undirected graphs (ESL)

vertex coloring (ADM)

vertex cover (ADM)

greedy algos

inference

after-model-selection-estimation (CSI)

inference & max likelihood (ESL)

inference frequentist (CSI)

parametric inference (PSC)

accuracy after model selection

selection bias

combined bayes-frequentist estimation

notes

selection bias

combined bayes-frequentist estimation

notes

inference & max likelihood (ESL)

inference frequentist (CSI)

parametric inference (PSC)

information theory

info theory tutorial (stone, USheffield)

finding a route

bits are not binary digits

entropy

entropy - continuous variables

max-entropy distributions

channel capacity

shannon's source coding theorem

noise reduces channel capacity

mutual info

shannon's noisy channel coding theorem

gaussian channels

fourier analysis

history

key equations

bits are not binary digits

entropy

entropy - continuous variables

max-entropy distributions

channel capacity

shannon's source coding theorem

noise reduces channel capacity

mutual info

shannon's noisy channel coding theorem

gaussian channels

fourier analysis

history

key equations

interviewing

label spreading, label propagation

latent variables

linear factor models (DLG)

probabilistic PCA + factor analysis

independent component analysis

sparse coding

manifold representation of PCA

independent component analysis

sparse coding

manifold representation of PCA

learning

linear models

linear programming

make

markov chains

matrix math

basics (DIDL)

determinants (LAY)

eigenvectors & eigenvalues (LAY)

inner-product-length-orthogonality (LAW)

linear algebra overview (DLG)

matrix cookbook (matrixcookbook.com)

matrix determinants (ADM)

matrix math (LAY)

matrix multiply (ADM)

matrix ops (ITA)

numerical basics (ADM)

symmetric matrices (LAY)

linear & matrix ops

eigen decompositions

single-variable calculus

multi-variable calculus

integrals

random variables

eigen decompositions

single-variable calculus

multi-variable calculus

integrals

random variables

determinants (LAY)

eigenvectors & eigenvalues (LAY)

intro; eigenvectors & difference equations

determinants & characteristic equations

similarity

diagonalization

eigenvectors & linear transforms

complex eigenvalues

discrete dynamical systems

differential equations

iterative estimates

determinants & characteristic equations

similarity

diagonalization

eigenvectors & linear transforms

complex eigenvalues

discrete dynamical systems

differential equations

iterative estimates

inner-product-length-orthogonality (LAW)

linear algebra overview (DLG)

scalars, vectors, matrices, tensors

vector|matrix multiplication

identity matrix

inverse matrix

linear dependence

span

norms

diagonal matrix

symmetric matrix

orthogonal matrix

eigen decomposition

singular value decomposition (svd)

moore-penrose pseudoinverse matrix

trace operator

determinant

example - principal components analysis (PCA)

vector|matrix multiplication

identity matrix

inverse matrix

linear dependence

span

norms

diagonal matrix

symmetric matrix

orthogonal matrix

eigen decomposition

singular value decomposition (svd)

moore-penrose pseudoinverse matrix

trace operator

determinant

example - principal components analysis (PCA)

matrix cookbook (matrixcookbook.com)

basics

derivatives

inverses

complex matrices

solutions & decompositions

multivariate distributions

gaussians

special matrices

functions & operators

1-D results

proofs

derivatives

inverses

complex matrices

solutions & decompositions

multivariate distributions

gaussians

special matrices

functions & operators

1-D results

proofs

matrix determinants (ADM)

matrix math (LAY)

matrix multiply (ADM)

matrix ops (ITA)

numerical basics (ADM)

linear equations

bandwidth reduction

matrix multiplication

determinants & permanents

optimization (constrained, unconstrained)

linear programming

random number gen

factors & prime testing

arbitrary-precision math

the knapsack problem

discrete fourier transforms (DFTs)

bandwidth reduction

matrix multiplication

determinants & permanents

optimization (constrained, unconstrained)

linear programming

random number gen

factors & prime testing

arbitrary-precision math

the knapsack problem

discrete fourier transforms (DFTs)

symmetric matrices (LAY)

max likelihood estimation (MLE)

methods

methodologies (paperswithcode)

representation learning; transfer learning; image classification; reinforcement learning; 2D classification; domain adaptation; data augmentation; ...

mixtures

latent linear models (SM)

factor analysis

principal components analysis (PCA)

choosing number of dimensions

PCA for categories

PCA for paired & multiview data

independent component analysis (ICA)

principal components analysis (PCA)

choosing number of dimensions

PCA for categories

PCA for paired & multiview data

independent component analysis (ICA)

monte carlo

monte carlo methods (DLG)

sampling; importance sampling; markov chain monte carlo (MCMC); gibbs sampling; mixing challenges

multilabel

natural language processing

Gensim lessons ()

NLP SOTA (paperswithcode)

natural language processing (NLP) (DIDL)

spaCy tutorial (spacy.io)

topic models (FDS)

NLP SOTA (paperswithcode)

595 tasks (july2022)

natural language processing (NLP) (DIDL)

spaCy tutorial (spacy.io)

topic models (FDS)

topic models

non-negative matrix factorization (NMF)

hard & soft clustering

latent dirichlet allocation (LDA)

dominant admixtures

math

term-topic matrices

hidden markov models

graph models & belief propagation

bayes|belief nets

markov random fields

factor graphs

tree algorithms

message passing

single-cycle graphs

single-loop belief updates

max weight matching

warning propagation

variable correlation

non-negative matrix factorization (NMF)

hard & soft clustering

latent dirichlet allocation (LDA)

dominant admixtures

math

term-topic matrices

hidden markov models

graph models & belief propagation

bayes|belief nets

markov random fields

factor graphs

tree algorithms

message passing

single-cycle graphs

single-loop belief updates

max weight matching

warning propagation

variable correlation

novelties-outliers

numerical analysis

cross decomposition (SKL)

cryptography (ADM)

diffeqs ordinary (NP)

diffeqs partial (NP)

dimensionality (ESL)

dimensionality (FDS)

dimensionality reduction (HoML)

dimensionality reduction (DMMD)

discrete fourier xform (ADM)

equation solving (NP)

integration (NP)

interpolation (NP)

optimization (ADM)

optimization (DIDL)

optimization (DLG)

optimization (NP)

optimization (SM)

polynomials & FFTs (ITA)

signal processing (NP)

splines (ESL)

summations (ITA)

canonical PLS (partial least squares)

SVD (simplified) PLS

PLS regression

SVD (simplified) PLS

PLS regression

cryptography (ADM)

diffeqs ordinary (NP)

diffeqs partial (NP)

dimensionality (ESL)

dimensionality (FDS)

dimensionality reduction (HoML)

dimensionality reduction (DMMD)

discrete fourier xform (ADM)

equation solving (NP)

integration (NP)

interpolation (NP)

optimization (ADM)

optimization (DIDL)

optimization (DLG)

optimization (NP)

optimization (SM)

polynomials & FFTs (ITA)

signal processing (NP)

splines (ESL)

summations (ITA)

numpy

advanced techniques (PDA)

basics (PDA)

numpy basics (PDSH)

vectors, matrices, ndarrays (NP)

ndarray internals

array manipulation

broadcasting

ufuncs

structured & record arrays

sorting

numba

advanced array I/O

performance tips

array manipulation

broadcasting

ufuncs

structured & record arrays

sorting

numba

advanced array I/O

performance tips

basics (PDA)

numpy basics (PDSH)

arrays; boolean arrays; broadcasting; indexing; sorting; structured data; aggregations; ufuncs; data types

vectors, matrices, ndarrays (NP)

pandas

pandas basics (PDA)

pandas basics (PDSH)

series; data frames; index objects; essential functions; descriptive stats

pandas basics (PDSH)

aggregation/grouping, concat, append, hierarchical indexes, merge, join, missing values, objects, ops, performance, pivot tables, time series ops, vectorized string ops

parametric models

performance

parallelism tips (SKL)

parallelization (pipelines) (DSCL)

performance tips (SKL)

scaling tips (SKL)

parallelization (pipelines) (DSCL)

serial processing

parallel processing

distributed processing

parallel processing

distributed processing

performance tips (SKL)

scaling tips (SKL)

planning

planning / capacity

probabilistic analysis

Probabilistic Analysis and Randomized Algorithms (ITA)

Indicator random variables, Randomized algorithms, Probabilistic analysis and further uses of indicator random variables

probability & statistics

bootstrap-confidence-intervals (CSI)

cheatsheet (WillChen)

cookbook ()

counting-probability (ITA)

distributions (PSC)

distributions multivariate (PSC)

expectation (PSC)

frequentist stats (SM)

hypothesis testing (PSC)

hypothesis testing - false discovery (CSI)

intro ()

medians (ADM)

medians orderstats (ITA)

modeling ()

other math (PSC)

probability (DLG)

probability (SM)

random vars (PSC)

statistics (NP)

stats glossary ()

survival analysis & EM (CSI)

theory (PSC)

variance (PSC)

cheatsheet (WillChen)

cookbook ()

counting-probability (ITA)

distributions (PSC)

distributions multivariate (PSC)

expectation (PSC)

frequentist stats (SM)

hypothesis testing (PSC)

hypothesis testing - false discovery (CSI)

intro ()

medians (ADM)

medians orderstats (ITA)

modeling ()

other math (PSC)

probability (DLG)

probability (SM)

random vars (PSC)

statistics (NP)

stats glossary ()

survival analysis & EM (CSI)

theory (PSC)

variance (PSC)

pycaret

PyCaret intro (BJP)

PyCaret is a high-level, low-code Python library that makes it easy to compare, train, evaluate, tune, and deploy machine learning models with only a few lines of code. At its core, PyCaret is basically just a large wrapper over many data science libraries such as Scikit-learn, Yellowbrick, SHAP, Optuna, and Spacy. Yes, you could use these libraries for the same tasks, but if you don’t want to write a lot of code, PyCaret could save you a lot of time.

recommenders

recurrent NNs

recursion

backtracking (AJE)

backtracking (JE)

recursion (JE)

backtracking (JE)

recursion (JE)

reductions

simplify & delegate

tower of hanoi

mergesort

quicksort

design pattern

recursion trees

linear-time selection

fast multiplication

exponentiation

simplify & delegate

tower of hanoi

mergesort

quicksort

design pattern

recursion trees

linear-time selection

fast multiplication

exponentiation

regression

additive-models-trees (ESL)

general linear models & regression trees (CSI)

isotonic regression (SKL)

jackknife (CSI)

linear models (OLS, ridge, lasso, AIC/BIC, elastic-net, LARS, OMP, Bayes, GLM, Tweedie) (SKL)

linear regression (ESL)

linear regression (PSC)

linear regression (SM)

logistic regression (SM)

metrics (SKL)

multiclass & multioutput algos (SKL)

regularization (DLG)

ridge regression (SKL)

ridge regression (CSI)

general linear models & regression trees (CSI)

isotonic regression (SKL)

jackknife (CSI)

linear models (OLS, ridge, lasso, AIC/BIC, elastic-net, LARS, OMP, Bayes, GLM, Tweedie) (SKL)

linear regression (ESL)

linear regression (PSC)

linear regression (SM)

logistic regression (SM)

metrics (SKL)

multiclass & multioutput algos (SKL)

multioutput

regressor chains

regressor chains

regularization (DLG)

ridge regression (SKL)

ridge regression (CSI)

reinforcement learning

RL with Tensorflow (HoML)

approximation-off-policy-methods (RL)

approximation-on-policy-control (RL)

approximation-on-policy-prediction (RL)

dynamic programming (RL)

eligibility traces (RL)

frontiers (RL)

markov finite (RL)

monte carlo (RL)

n-step bootstrap (RL)

policy gradients (RL)

reinforcement learning ()

tabular method planning (RL)

temporal distances (RL)

approximation-off-policy-methods (RL)

approximation-on-policy-control (RL)

approximation-on-policy-prediction (RL)

dynamic programming (RL)

eligibility traces (RL)

frontiers (RL)

markov finite (RL)

monte carlo (RL)

n-step bootstrap (RL)

policy gradients (RL)

reinforcement learning ()

tabular method planning (RL)

temporal distances (RL)

restricted boltzmann machines

robotics

searching & sorting

all-pairs-shortest-paths (ITA)

all-pairs-shortest-paths (JE)

binary-search-trees (ITA)

combinational search (ADM)

depth first search (AJE)

depth first search (JE)

heapsort (ITA)

linear time sort (ITA)

quicksort (ITA)

searching (ADM)

searching (ADM)

similarity search (DMMD)

single source shortest paths (ITA)

sort search (EA)

sorting & searching (ADM)

summary (ADM)

topological sort (ADM)

all-pairs-shortest-paths (JE)

binary-search-trees (ITA)

definition

query

insert, delete

random build

query

insert, delete

random build

combinational search (ADM)

depth first search (AJE)

depth first search (JE)

heapsort (ITA)

linear time sort (ITA)

quicksort (ITA)

searching (ADM)

searching (ADM)

similarity search (DMMD)

single source shortest paths (ITA)

sort search (EA)

sorting & searching (ADM)

summary (ADM)

topological sort (ADM)

set theory

streams

strings

survival analysis

svms

support vector machines (ESL)

support vector machines (SVMs) (SKL)

svms (HoML)

support vector machines (SVMs) (SKL)

classification (SVC, NuSVC, LinearSVC)

multiclass SVM

scoring & metrics

weighted classes/samples

regression (SVR, NuSVR, LinearSVR)

complexity

kernels

precomputed kernels - the Gram matrix

multiclass SVM

scoring & metrics

weighted classes/samples

regression (SVR, NuSVR, LinearSVR)

complexity

kernels

precomputed kernels - the Gram matrix

svms (HoML)

sympy

intro (NP)

symbols; expressions; numeric evaluation; calculus (derivatives, integrals, series expansions, limits, sums & products); equation solvers; linear algebra

time series

Prophet (Facebook)

calendar math (ADM)

time series (PSC)

time series applications (SOTA) (paperswithcode)

time series ops (PDA)

calendar math (ADM)

time series (PSC)

time series applications (SOTA) (paperswithcode)

time series ops (PDA)

date & time datatypes; ranges, frequencies & shifting; periods; frequency conversion; moving windows

tools

common languages (DSCL)

creating one-liners (DSCL)

installation (SKL)

jq (JSON) basics ()

libraries (DSA)

list of tools (expanded) (DSCL)

mapreduce (DMMD)

patsy, statsmodels & scikit-learn (PDA)

python - pandas (NP)

resources (ADM)

resources (ADM)

statsmodels, patsy (NP)

Jupyter, R, Python, Rstudio, spark

creating one-liners (DSCL)

one-liners to scripts

creation using python or R

creation using python or R

installation (SKL)

jq (JSON) basics ()

libraries (DSA)

list of tools (expanded) (DSCL)

mapreduce (DMMD)

patsy, statsmodels & scikit-learn (PDA)

python - pandas (NP)

resources (ADM)

resources (ADM)

statsmodels, patsy (NP)

topology

hyperbolic topology (GT)

surfaces (GT)

three-manifolds (GT)

groups; spaces; manifolds; thick-thin decomposition; sphere at infinity

surfaces (GT)

intro; teichmuller spaces; surface diffeomorphisms

three-manifolds (GT)

topology; seifert manifolds; construction; the "eight geometries"; mostow rigidity problem; hyperbolic 3Ms; hyperbolic dehn filling

training

use cases

Google PageRank (ESL)

advertising (DMMD)

applications (DLG)

applications (DSA)

applications (RL)

audio algos (paperswithcode)

code generation algos (paperswithcode)

game-playing algos ()

medical applications (SOTA) (paperswithcode)

music papers (paperswithcode)

product embedding - ecommerce (arxiv)

speech algos (paperswithcode)

advertising (DMMD)

applications (DLG)

applications (DSA)

applications (RL)

audio algos (paperswithcode)

code generation algos (paperswithcode)

game-playing algos ()

medical applications (SOTA) (paperswithcode)

music papers (paperswithcode)

product embedding - ecommerce (arxiv)

speech algos (paperswithcode)

vision

computer vision SOTA (paperswithcode)

developers tools (scikit-image)

edges & lines (scikit-image)

exposures & colors (scikit-image)

filtering & restoration (scikit-image)

image datasets (scikit-image)

longform examples (scikit-image)

numpy basic ops (scikit-image)

object detection (scikit-image)

object segmentation (scikit-image)

transforms & registration (scikit-image)

1300 tasks (july2022)

developers tools (scikit-image)

edges & lines (scikit-image)

contour finding

convex hulls (binary images)

canny filters

marching cubes

ridge operators

active contour model

drawing std shapes

random shapes

hough transforms (straight line)

approximating & subdividing polygons

hough transforms (circular, elliptical)

skeletonizing

morphological thinning

edge operations (multiple)

convex hulls (binary images)

canny filters

marching cubes

ridge operators

active contour model

drawing std shapes

random shapes

hough transforms (straight line)

approximating & subdividing polygons

hough transforms (circular, elliptical)

skeletonizing

morphological thinning

edge operations (multiple)

exposures & colors (scikit-image)

RGB-grayscale conversions

RGB-HSV conversions

histogram matching

(ex) immunohistochemical (IHC) staining

adapting grayscale filters to RGB images

regional maxima filtering (bright features)

local histogram equalization (LHE)

gamma & log-contrast adjustments

histogram equalization

tinting grayscale images

RGB-HSV conversions

histogram matching

(ex) immunohistochemical (IHC) staining

adapting grayscale filters to RGB images

regional maxima filtering (bright features)

local histogram equalization (LHE)

gamma & log-contrast adjustments

histogram equalization

tinting grayscale images

filtering & restoration (scikit-image)

image datasets (scikit-image)

longform examples (scikit-image)

numpy basic ops (scikit-image)

object detection (scikit-image)

object segmentation (scikit-image)

transforms & registration (scikit-image)

visualization

catscatter scatterplot demo (myriam barnes)

clifford attractors ()

data exploration / visualization (DSCL)

display objects (SKL)

hypertools (hypertools.readthedocs.io)

inspection plots (SKL)

matplotlib basics (NP)

partial dependence plots (PDPs) (SKL)

permutation feature importance plots (SKL)

plotting & visualization (PDA)

receiver operating characteristic (ROC) curves (SKL)

seaborn basics (PDSH)

voronoi diagrams (ADM)

clifford attractors ()

simple demo using ggplot

data exploration / visualization (DSCL)

headers

descriptive stats

visuals / chart types

descriptive stats

visuals / chart types

display objects (SKL)

hypertools (hypertools.readthedocs.io)

inspection plots (SKL)

matplotlib basics (NP)

partial dependence plots (PDPs) (SKL)

permutation feature importance plots (SKL)

plotting & visualization (PDA)

matplotlib primer; pandas & seaborn; other tools

receiver operating characteristic (ROC) curves (SKL)

seaborn basics (PDSH)

intro and pokemon tutorial

voronoi diagrams (ADM)

wavelets