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
Book chapter summaries - deep learning, machine learning, various math
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