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scikit-learn (jupyter nb to HTML)
search-sort
set theory
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supervised learning
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algorithm theory
NP complete (ITA)
NP hard (AJE)
NP hard (JE)
complexity, big-O, growth rates, logarithms (ADM)
computational performance (Dive into DL)
divide & conquer (ITA)
factoring & primality testing (ADM)
methodology (Goodfellow)
model metrics & assessment (ESL)
notations & function types (ITA)
summation basics (ITA)
wavelets (FDS)
wavelets (Wikipedia)
approximations
approximate inference approaches (Goodfellow)
intro to approximation algos for NP-complete problems (ADM)
intro to approximation algos for NP-complete problems (ITA)
bandit algorithms
UCB - asymptotic optimality
UCB - bernoulli
UCB - minimax
adversarial vs stochastic linear bandits
basis expansions & regularization
bayes
exp
exp3 IX
exp3 adversarial linear
exploration
index
info theory
intro
least sqaure confidence bounds
least squares
lower bounds
lower bounds high probability
lower bounds instance dependent
lower bounds minimax
markov decisions
mirror descent
non-stationary
partial monitoring
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probability
ranking
stochastic finite
stochastic linear
stochastic linear asymptotic
stochastic linear finite
stochastic linear minimax
stochastic linear sparsity
stochastic markov
thompson sampling
upper confidence bound (UCB)
bayes
bayes
bayes stats
cheatsheet chapter
directed graphs (bayes nets)
empirical estimation
intro
boosting
additive trees
random forest boosting
bootstrap confidence intervals
probability-statistics
calculus
integration
interpolation
cheatsheets
SCDL
cliques
cliques
clustering
overview
overview
complete books
ADM
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component analysis
pca
cross validation
intro
data structures
ADM
B trees
augmenting
dictionaries
dictionaries
disjoint
fibonacci heaps
hashes
heaps
intro
lists
other
decision theory
cheatsheet (PSC)
deep learning architectures
CNN cheatsheet
CNNs
CNNs
CNNs - modern
Feedforward nets
GANs
GANs - discrete
Generative models
Intro
Intro
RNNs
RNNs
attention mechanics
autoencoders
cheatsheet
deep dive
intro
modern practices
representation learning
research, p3
rnn cheatsheet
rnns
rnns - modern
deep learning math
computation factors
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differential equations
ordinary (ODEs)
partial (PDEs)
dimensionality
ESL
FDS
reduction
dynamic programming
ADM
AJE
ITA
JE
RL
ensembles
ESL
random forests
expection maximization
survival analysis
feature engineering
model selection techniques
frequent itemsets
market baskets, a-priori, PCY, limited-pass algos, stream counts (DMMD)
gaussian mixtures
mixture models & EM
gaussian models
SM
geometry
bin packing
convex hulls
intersection detection
intro
line arrangements
medial axis transforms
minkowski sum
point location
polygon partitioning
polygon simplification
primitives
shape similarity
topology (book)
triangulation
vector spaces
glossaries
glossary
glossary
glossary
other topics
graphs
algorithms
bandwidth reduction
connected components
data structures
drawing
edge coloring
edge connectivity
feedback edge vertex set
flows & cuts
generating
hamiltonian cycles
intro (JE)
isomorphism
maxflow
minimum spanning trees
minimum spanning trees
minimum spanning trees
minimum spanning trees
network flow
pagerank
partitions
planarity detection
polynomial time
random graphs
social graphs
sparse data
summary
transitive closure
traversal
undirected graphs
vertex coloring
vertex covers
weighted
greedy algorithms
intro
intro
intro
inference
inference - parametric
inference after model selection
intro (PSC)
interviewing
101 DS questions
kernels
SM
smoothing
learning
convergence
techniques
linear algebra
ADM
DLG
book
determinants
eigenvectors, eigenvalues
equation solving
inner product length & orthogonality
intro
linear algebra
linear programming
vector | matrix | ndarray ops
linear models
generalized linear models & regression trees
linear NNs
linear classification methods
linear factor models
machine learning tutorials
basics
cheatsheet
glossary
intro
intro
markov
MCMC (CSI)
MCMC (SM)
markov chains
random walks - markov chains
matrix math
cookbook
determinants
math ops
matrix multiplication
matrix ops
sparse data
symmetric
max likelihood estimation
intro
mixture models
latent (inferred) linear models
natural language processing
intro
topic models
nearest neighbors
intro
prototypes
number theory
arbitrary precision math
combinatorials
complex numbers
cryptography
intro
permutations
random numbers
satisfiability
optimization
constrained & unconstrained opt
optimization
optimization (NP)
overview
other
structured probabilistic models
parallelism
intro
perceptrons
intro
performance
multithreading
planning algorithms
intro
motion planning
polynomials
discrete fourier transform (DFT)
splines
probability, statistics
cheatsheet
cheatsheet
cookbook
counting & probability basics
distributions
expectation
hypothesis testing
hypothesis testing - false discovery
inequalities
intro
intro (Goodfellow)
intro (PSC)
intro (SM)
medians
medians, orderstats
multivariate distributions
other math
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prob | stats | modeling
random variables
randomized
the partition problem
variances
queues
job scheduling
priority queues
priority queues
queues & sequences
randomization
monte carlo
monte carlo
recommenders
intro
recommenders
recursion
backtracking
intro
regression
LR (ESL)
LR (PSC)
LR (SM)
jackknife
lasso
logistic regression
regularization
ridge
reinforcement learning
approximation on policy control
approximation on policy prediction
eligibility traces
markov-finite
n-step bootstrapping
off-policy methods
policy gradients
reinforcement learning ebook, dec2020
tabular method planning
temporal distances
sampling
sampling
scikit-learn (jupyter nb to HTML)
biclustering
calibration curves
classification metrics
clustering
combination workflows (pipelines & feature unions)
component analysis | matrix factorization
covariance
cross decomposition
cross validation
datasets - artificial data generators
datasets - openML, more
datasets - toy datasets
decision trees
density estimation
discriminant analysis (PCA, LDA)
ensembles-bagging
ensembles-boosting
ensembles-stacking
ensembles-voting
feature engineering | data imputation
feature engineering | preprocessing
feature engineering | random projections
feature extraction (general)
feature extraction (images)
feature extraction (text)
feature selection
file IO
gaussian mixture models (GMMs)
gaussian models
hyperparameter tuning
kernel approximations
manifold models
metrics basics
multiclass & multioutput solvers
multilabel metrics
multilayer perceptrons (MLPs)
naive bayes
nearest neighbors
novelty & outlier detection
pairwise ops
performance vs parallelism
performance vs problem type
performance vs scaling
regression (isotonic)
regression (kernel ridge)
regression (linear)
regression (logistic)
regression metrics
restricted boltzmann machines (RBMs)
stochastic gradient descent (SGD)
support vector machines (SVMs)
unsupervised learning overview
validation & learning curves
visualization | display objects (scikit-learn)
visualization | inspection plots
visualization | partial dependence plots
visualization | permutation feature importance plots
visualization | roc curve plots
search-sort
all-pairs shortest path
all-pairs shortest path
chinese postman
combinational search
depth first
depth-first
intro (ADM)
intro (EA)
methods
range search
search
shortest path
shortest path - single source
similarity search
sorting
traveling salesman
set theory
finite state machines
knapsack problem
set cover
set packing
sets
sets
sets (independent)
subsets
signal processing
fourier analysis
signal processing
singular value decomposition
svd
sorting
heapsort
linear-time sort
quicksort
topological
spatial data
DSA
stochastic processes
stochastic processes
streams
streams
strings
LCS
shortest common superstring
string matching
string matching
string matching (approx)
text compression
supervised learning
overview
support vector machines
kernels
overview
survival analysis
survival analysis
symbolic math
intro
tbd
exponentials
frequentist inference
frequentist stats
function estimation
inference averaging
matching
partitions
vision-dive
time series
amortized analysis
calendar math
cheatsheet
tools
Numba, Cython
Python-Pandas
command line cheatsheet
libraries
mapreduce
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python file I/O
resources
statsmodel
tree structures
K-D trees
additive models
binary search trees
drawing
intro
red-black trees
steiner trees
suffix trees
van emde boas trees
unsupervised learning
summary
use cases
advertising
applications
applications
applications
visualization
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voronoi diagrams