Index

[SYMBOL][A][B][C][D][E][F][G][H][I][J][K][L][M][N][O][P][Q][R][S][T][U][V][W][X][Y][Z]

SYMBOL

@ operator
2-grams (bigrams)
2D thetas
2D vectors
3-grams (trigrams)
3D vectors2nd
3x3 filter
4-grams (quadruplets)
4x4 matrix

A

ACK (acknowledgement) signal
activation functions2nd
Adam
additive smoothing
AffNIST
affordance
AGI (artificial general intelligence)2nd
AI (artificial intelligence)2nd
AI Response Specification (AIRS)
aichat bot framework
aichat project
AIML (Artificial Intelligence Markup Language)2nd
  
pattern-matching chatbots with
    AIML 1.0
    AIML 2.0
    Python AIML interpreter
<aiml> tags
aiml_bot package
AIRS (AI Response Specification)

algorithms
  
for scoring topics
  
optimizing
    discretizing
    indexing
  
search algorithms
Amazon Alexa2nd
Amazon Echo
Amazon Lex2nd
Amazon Web Services.
    See AWS.
AMIs (Amazon Machine Images)
Anaconda3 distribution
analogies
Analyze processing stage
angular distance
ANN (approximate nearest neighbors)2nd
ANN (artificial neural network)2nd
Annoy package
  
Python
  
Spotify
AnnoyIndex object
Apache Lucern + Solr
API (application programmer interface)
approximate grep
approximate nearest neighbors.
    See ANN.
apt package
apt-get update command
ARMA (auto-regressive moving average) model
arrays
artificial general intelligence.
    See AGI.
artificial intelligence.
    See AI.
artificial neural network.
    See ANN.
attention mechanism
augmenting data
auto-regressive moving average model.
    See ARMA.
autoencoders2nd
automata
average option, pooling
awards
AWS (Amazon Web Services)2nd

AWS (Amazon Web Services) GPU instances
  
controlling costs
  
creating
AWS Billing Dashboard
AWS Budget Console
AWS Management Console
Axes3D class
axon

B

backpropagation2nd3rd4th5th6th
BallTree
Basic Linear Algebra Subprograms library.
    See BLAS.
Bastion host
batch learning
batch normalization
batch_size2nd
Bayesian searches
BeeSeek search engine
bias
  
avoiding
  
backpropagation
  
cost functions
  
differentiable functions
  
logical OR statements
  
loss functions
  perceptrons
    
limitations of
    training
  
Pythonic neurons
bias_weight feature
bidirectional recurrent networks
bigram scoring function
bigrams2nd
Billing Dashboard, AWS
binary_crossentropy function2nd3rd4th
BLAS (Basic Linear Algebra Subprograms) library2nd
bot-to-bot conversations
BOW (bag-of-words)2nd3rd4th
  
measuring
  
overlap
brittle pattern-matching
Brown Corpus
bucketing
Budget Console, AWS
built-in functions

C

Caffe framework
candidate gate
Cartesian distance
case folding
case normalization
casual_tokenize function2nd3rd4th
categorical_crossentropy function2nd
CBOW (continuous bag-of-words)2nd
CEC (constant error carousel)
cells
CFG (context-free grammars)
chain rule
character classes2nd3rd
character prediction
character range
character sequence matches
character-based model
character-based translator
chatbots2nd
  
approaches to
  
assembling models for sequence generation
  
building character dictionary
  
building using FSM
  
combining approaches
  
connecting users
  
conversing with
  
design process
  
emotional responses
  
generating one-hot encoded training sets
  
generating responses
  
generative models
  
grounding
  
language skill
    community management
    conversational chatbots
    customer service
    hybrid approaches
    marketing chatbots
    modern approaches
    question answering dialog systems
    therapy
    virtual assistants
  
non sequiturs
  
pattern-matching approach
    network view of
    with AIML
  
pipelines
  
popular responses
  
predicting sequences
  
preparing corpus for training
  
questions with predictable answers
  
referring to search engines
  
search-based
    context
    example of
  
sequence-to-sequence, training
  
Will chatbot framework
ChatterBot
checkpointing keyword argument
Chloe, Aira.io
class labels
classifiers
CNNs (convolutional neural networks)2nd
  
architecture of
  
filter composition
  
frameworks for
  
kernels
  
narrow convolutions
    Dropout layers
    implementation in Keras
    models in pipelines
    pooling
    sigmoid activation function
    training models
  
padding
  
step size
  
training
  
word order
CNTK environment
co-occurrence matrix
CodingBat
collections module
collections.Counter object
collinear warning
commas
community management chatbots
competitions
compile method
compiling models
components attribute
computational graphs
Compute Unified Device Architecture.
    See CUDA.
conda environment
<condition> tag
confusion matrix
connectionist theory
constant error carousel.
    See CEC.
constant RAM algorithms
  
gensim models
  
graph computing
context-free grammars.
    See CFG.
contractions
Conv1D layer
convergence
conversational chatbots2nd
convex error curve
convolution tools, Keras
convolutional neural networks.
    See CNNs.
copy.copy() function
CopyClip
Core ML documentation, Apple
CoreNLP library
cosine distance2nd3rd4th5th
cosine similarity2nd3rd
cost functions
Counter dictionary2nd
courses
create_projection function
CRM (customer relationship management system)
cross-validation
CUDA (Compute Unified Device Architecture)
CuDNNLSTM
curly braces2nd
customer relationship management system.
    See CRM.
customer service chatbots

D

DAG (directed acyclic graph)2nd
dark patterns
dashes2nd

data
  
augmenting
  
labeled
  
selecting
data-driven programming
DataCamp
DataFrame2nd
datasets2nd
  
preparing for sequence-to-sequence training
  
scaling
  
shuffling
dates, extracting
dateutil.parser.parse
DBpedia
decode_sequence function
decoder rings
decoder_input_data
decoder_outputs
decoder_target_data
decoding thought
deep learning2nd
dendrites
denominations
denormalized data
desired outcomes
DetectorMorse
deterministic finite automaton.
    See DFA.

developer tools
  
for Mac
  
installing
DFA (deterministic finite automaton)2nd
dialog engines2nd.
    See also chatbots.
    See also chatbots.
dialog system
DialogFlow, Google2nd
dict keys
dict objects
dictionaries2nd
differentiable functions
dimension reduction
dimensionality
directed acyclic graph.
    See DAG.
Dirichlet distribution
discrete vectors
discretizing
distance functions
distance metrics
distances2nd
  
cosine distance
  
Euclidean distance
  
Manhattan distance
distributed search engines
Doc2vec algorithm
Docker platform
document vectors2nd
documenting similarity with Doc2vec
documents, loading
Donne Martin’s Coding Challenges
dot products
double quotes
double-backslash
dropout
Dropout layers2nd3rd
Dropout(percentage)
Duck Duck Go search engine2nd3rd4th
Duplex system, Google

E

EarlyStopping
edit distance2nd
eigenvalues
Elastic Search
ELIZA dialog engine2nd
embedding_dims value
embeddings2nd3rd4th
encoder-decoder architecture
  
autoencoders
  
decoding thought
end-of-sequence token
entity names
epochs parameter2nd
error carousel
error derivative
error matrix
error surface
errors, backpropagating
Euclidean distance2nd3rd4th
Euclidean spaces
evaluate method
exclamation point
Execute processing stage
exp() function
exploding gradient problem

extracting
  
dates
  
GPS locations
  
information2nd
  
numerical data
  
relationships
    entity name normalization
    normalizing
    POS tagging
    segmentation
    sentence segmentation with regular expressions
    split
    word patterns

F

f-string template
Facebook2nd
FALCONN (Fast Lookup of cosine and Other Nearest Neighbors)
Fast Lookup of Approximate Nearest Neighbors.
    See FLANN.
fastText library2nd
features, choosing
feed-forward network2nd3rd
feedback, steering with
file openers
filters
  
composition of
  
shapes of
.findall() function
finite state machine.
    See FSM.
finite state transducer.
    See FST.
fit method2nd
fit_generator method
fit_transpose
.fit() method2nd3rd
flags
FLANN (Fast Lookup of Approximate Nearest Neighbors)
Flatten() function
flip_sign function
floating point values
flush() method
folds
for loops
forget gate
forward pass
fractal dimensions
fractional distance
frameworks for CNNs
frequency based models
frequency vectors
from_records() function
FSM (finite state machine)2nd3rd4th5th
  
alternatives to
  
building chatbot using
  
regular expressions
FST (finite state transducer)2nd
full text search
fully connected network
fuzzy matching2nd
fuzzywuzzy package

G

GANs (generative adversarial networks)
gated recurrent unit.
    See GRU.
gated recurrent units
gem package
generalization
Generate processing stage
generative adversarial networks.
    See GANs.
generative models
  
chatbot approaches
  
transfer learning
generators2nd
genetic algorithms
gensim models
gensim package
gensim Vocab object
gensim Word2vec model
gensim.KeyedVectors model type
gensim.KeyedVectors.most_similar() method
gensim.LsiModel
gensim.models.KeyedVector class
gensim.models.LsiModel2nd
gensim.Word2vec modules
geographic information system.
    See GIS.
get() method
Gigablast search engine2nd
GIS (geographic information system)2nd
GitGUI
glob patterns
Global Vectors vs. Word2vec
GlobalMaxPooling1D layer

glossary
  
acronyms
  
terms
GloVe (Global Vectors)2nd3rd
go_backwards keyword argument
Google Adwords Auction case study
Google Assistant2nd
Google Dialogflow
Google Home
Google News
Google Ngram Viewer2nd
Google Now
Google Scholar
Google Translate pipeline
Google’s Allo
GPS locations, extracting
GPUs (graphical processing units)2nd
  
controlling costs of AWS GPU instances
  
creating AWS GPU instances
  
rental options
  
renting vs. buying
  
training NLP models on
gradient descent
grammar2nd3rd4th
graph computing
graphemes
graphical processing units.
    See GPUs.
grep application
grid searches
grounding2nd
grouping parentheses
groups
GRU (gated recurrent unit)2nd

H

h5py package
half pipes
hand-coded algorithms
hand-crafted weights
handlebars
hardcoded patterns
hashes
help() function
hidden weights
Hierarchical Navigable Small World.
    See HNSW.
high performance computing.
    See HPC.
high-dimensional continuous vectors
high-dimensional data
high-dimensional indexing
high-dimensional space
high-dimensional vectors
  
hashes
  
high-dimensional thinking
    1D index
    2D, 3D, 4D indexes
  
vector space indexes
HipChat2nd
HNSW (Hierarchical Navigable Small World)2nd
Homebrew package manager
homographs
homonyms
homophones
horizontal scaling
HPC (high performance computing)2nd3rd
hyperbolic tangent function
hyperparameters2nd3rd4th5th6th
hyphens

I

i18n (internationalization)
IDE (integrated development environment)2nd
IDF (inverse document frequency)
if statements
imbalanced training set
IMDB data
IncrementalPCA model
independent tokens
index scan
indexing
  
1D index
  
2D, 3D, 4D indexes
  
benefits of
  
high-dimensional
  
open source full-text indexers
  
vector space indexes
  
with Annoy
India Technical University.
    See ITU.
indiscerniblity property
inference stage
information extraction2nd
information retrieval.
    See IR.
init_sims method
initial_state argument
inner product
input_characters
input() function

installing
  
NLPIA
  
Will chatbot framework
integrated development environment.
    See IDE.
internationalization.
    See i18n.
inverse document frequency.
    See IDF.
inverse proportionality
inverted index2nd
IR (information retrieval)2nd3rd
ITU (India Technical University)

J

Jaccard distance
junk mail
jupyter project

K

K output nodes
K-Dimensional Trees
k-fold cross-validation
k-means
Kaggle competitions
KD-Tree algorithm
keras API
Keras library2nd3rd
  
CNNs with
  
RNNs with
  
sequence-to-sequence models in
kernels2nd
key-value stores
KeyedVectors object2nd3rd
KeyedVectors.vocab dict
keyword searches
killer app, Word2vec
knowledge base
knowledge graph inference
Kronuz/Xapiand

L

L1 regularization
L2 regularization
labeled data
language modeling
Laplace smoothing
Lasagne library2nd
latent semantic analysis.
    See LSA.
latent semantic indexing.
    See LSI.
latent semantic information
layer inputs, LSTM
layers, pooling
LDA (linear discriminant analysis)2nd3rd4th
  
classifiers
  
LDiA and
LDiA (Latent Dirichlet allocation)2nd3rd4th
  
as spam classifier
  
for SMS messages
  
history of
  
LDA and
  
LSA vs.
ldia_topic_vectors matrix
learning rate2nd
left singular vectors
lemmatization2nd
Lemur Project Components
Levenshtein distance2nd
Levenshtein’s algorithm
lexers
lexicons
likeability, predicting
limit keyword argument
linear algebra2nd3rd
linear discriminant analysis.
    See LDA.
linearly separable data
list comprehensions
locality sensitive hashing.
    See LSH.
location invariance
log() function
logical OR statements
long short-term memory.
    See LSTM.
loss functions2nd3rd
low-dimensional continuous vectors
lower() function
LSA (latent semantic analysis)2nd3rd4th5th
  
enhancements to
  
for spam classification
  
LDiA vs.
  
Word2vec vs.
LSH (locality sensitive hashing)2nd3rd4th
  
high-dimensional indexing
  
high-dimensional vectors
    hashes
    high-dimensional thinking
    vector space indexes
  
predicting likeability
LSHash package
LSI (latent semantic indexing)2nd
LSTM (long short-term memory)2nd3rd4th
  
backpropagation
  
gated recurrent units
  
generating novel text
  
modeling language
  
optimization of
  
stacking layers
  
training models
  
tuning models
  
unknown tokens
  
using novel words
  
with peephole connections
Lua package

M

M neurons
Mac OS (operating system)
  
developer tools
  
package manager
  
tuneups
machine learning
  
avoiding bias
  
batch normalization
  
building models
  
cross-validation
  
data selection
  
dropout
  
imbalanced training sets
    augmenting data
    oversampling
    undersampling
  
labeled data
  
overfit
  
performance metrics
    measuring classifier performance
    measuring regressor performance
  
regularization
  
underfit
machine translation
machine-generated text
Mahalanobis distance
Management Console, AWS
Manhattan distance2nd
manipulative search engines
mapping in Python
marketing chatbots2nd
mathematical notation
matplotlib package
matrix orientation
matrix product
matrix, transpose of
max pooling
max_training_samples
maxlen variable2nd3rd4th
mean squared error.
    See MSE.

measuring
  
bag of words
  
performance of classifiers
  
performance of regressors
  
system capability
Meld tool
MIH (multi index hashing)2nd
mini-batch training
Minkowski distance
MinMaxScaler
Mitsuku chatbot
MNIST dataset
model parameters2nd
model training
model.fit() function
model.summary() function2nd

models
  
assembling models for sequence generation
  
batch normalization
  
building
  
compiling
  
dropout
  
in pipelines
  
regularization
  
sequence-to-sequence in Keras
  
training2nd3rd
    on GPUs
    reducing memory footprint during training
  
tuning
momentum model
morphemes2nd3rd
most_common() function
most_similar method
MSE (mean squared error)2nd
multi index hashing.
    See MIH.
multi-resolution grid searches
multidimensional semantic vectors
multilayer perceptron

N

n-character grams
n-grams2nd3rd4th
  
overview of
  
stop words
Naive Bayes algorithm2nd
named entities
  
information extraction
  
knowledge base
Natural Language Forensics
natural language generation.
    See NLG.
natural language processing.
    See NLP.
Natural Language Toolkit.
    See NLTK.
natural language understanding.
    See NLU.
negative argument
negative class labels
negative sampling
NELL (Never Ending Language Learning)2nd3rd
nessvector2nd
net
network
neural networks
  
bias
    backpropagation
    cost functions
    differentiable functions
    logical OR statements
    loss functions
    Pythonic neurons
  
deep learning
  
error surface
  
in Python
  
Keras
  
mini-batch training
  
nonconvex error curves
  
normalization
  
perceptrons
  
stochastic gradient descent
neuron2nd
Never Ending Language Learning.
    See NELL.
new weights
ngrams function
NLG (natural language generation)
NLP (natural language processing)2nd3rd
  
chatbot pipelines
  
measuring system capability
  
overview of
    building chatbots
    extracting numerical data
  
parallelizing computations
    GPU rental options
    renting GPUs vs. buying
    Tensor processing units
    training NLP models on GPUs
  
pipeline layers
  
practical applications for
  
programming languages vs.

NLPIA
  
automatic environment provisioning procedures
  
installing
nlpia package2nd3rd4th5th6th7th
nlpia README file
nlpia.data.loaders.get_data() method2nd
nlpia/data directory
nlpiaenv environment
NLTK (Natural Language Toolkit)
nltk.casual_tokenizer
nltk.tag package
NLU (natural language understanding)2nd3rd
NMF (nonnegative matrix factorization)2nd
NMSLIB (Non-Metric Space Library)
non-ASCII characters
nonconvex error curves
nonlinear activation function
nonlinear dimension reduction
nonlinearly separable data
nonnegative matrix factorization.
    See NMF.
nonnegativity property
nonspam SMS messages
normalized dot product
normalizing
  
batches
  
entity names
  
relations
  
vocabulary
    case folding
    lemmatization
    stemming
    use cases
novel text
np.matmul() function
NULL characters
num_bytes function
num_encoder_tokens
num_neurons layer
num_neurons parameter
num_rows function
numerical data, extracting
numerical perceptrons
numpy arrays2nd3rd
Nutch

O

O(N) algorithm
objective function
Okapi BM25 ranking function
one-dimensional filter shape
one-hot column vector
one-hot encoded training sets
one-hot encoded vectors
one-hot row vector
one-hot vectors
one-way neural network
Open Mind Common Sense
Open Source Full Text Search Engine
open source full-text indexers
open source projects
open source search engines
OpenFST
OpenStreetMap

optimizing
  
LSTM
  
NLP algorithms
    discretizing
    indexing
OR statement
OR symbol2nd
ord values
OrderedDict
original weights
output gate
output nodes
output words
output_vocab_size
output_vocabulary
overfitting
  
reducing
  
training samples
oversampling

P

package manager for Mac
pad characters
pad_sequences method
padding
Pandas DataFrames2nd3rd4th
parallelizing NLP computations
  
GPU rental options
  
renting GPUs vs. buying
  
tensor processing units
  
training NLP models on GPUs
parentheses2nd
Parse processing stage
parts of speech.
    See POS.
pattern matching algorithms
  
information extraction
  
regular expressions
pattern-matching chatbots2nd3rd
  
network view of
  
with AIML
    AIML 1.0
    AIML 2.0
    Python AIML interpreter
pattern_response dictionary
patterns of words
PCA (principal component analysis)2nd3rd4th5th6th
  
for SMS message semantic analysis
  
LSA for spam classification
    LSA enhancements
    SVD enhancements
  
NLP (natural language processing)
  
on 3D vectors
  
truncated SVD for SMS message semantic analysis
PCA.fit() method
pd.DataFrame printouts
peephole connections
PEP8 program
perceptrons
  
limitations of
  
numerical
  
training
performance metrics
  
measuring classifier performance
  
measuring regressor performance
periods
phrase-terminating punctuation
PhraseMatcher
Pierson correlation
pip (pip installs pip)

pipelines
  
chatbots
  
convolutional
  
layers of
  
models in
  
sequence-to-sequence, assembling
    assembling networks
    models in Keras
    preparing datasets for training
    sequence encoders
    thought decoders
PiQASso
Plotly wrapper
plus sign
Poisson distribution
polarity
polysemy
pooling
Porter stemmer
POS (parts of speech)2nd3rd4th
positive argument
positive class labels
PostgreSQL database2nd
PR (pull request)
precision2nd
predicate
predict method2nd3rd
.predict_class() method
predict_classes method
.predict() method2nd
predicting
  
bidirectional recurrent neural networks
  
likeability
  
sequences
  
statefulness
pretrained word vector
principal component analysis.
    See PCA.
probabilities_list function
processing stages, chatbot
product() function
programming languages vs. NLP
Project Gutenberg dataset
project ideas
projected distance
propagation
pull request.
    See PR.
punctuation sequences
pyfst interface

Python programming language
  
AIML interpreter
  
dict objects
  
mapping in
  
mastering
  
neural networks in
  
neurons in
  
OrderedDict
  
strings and
  
strings, types of
  
style conventions
  
templates in
PyTorch framework2nd3rd4th

Q

QDA (quadratic discriminant analysis)2nd
quadruplets
queries, semantic
question answering (QA) systems2nd3rd
quotes2nd

R

random guessing
random projection
random searches
<random> tag
random values
rare n-grams
raw data
raw strings
RBMs (restricted Boltzmann machines)
re package2nd
re.compile() function
re.split function2nd
read–evaluate–print loop.
    See REPL.
real-value indexes
recall2nd
rectified linear unit.
    See ReLU.
recurrent neural networks.
    See RNNs.
Regex OR symbol
regex package2nd3rd4th
RegexpTokenizer function
regressors, measuring performance of
regular expressions2nd3rd4th
  
character classes
  
groups
  
OR symbol
  
overview of
  
sentence segmentation with
  
separating contractions
  
separating words
regularization
relationships
  
between words
  
extracting
    entity name normalization
    normalizing
    POS tagging
    segmentation
    sentence segmentation with regular expressions
    split
    word patterns
  
information extraction
  
knowledge base
relevance ranking
ReLU (rectified linear unit)2nd3rd

renting GPUs
  
buying vs.
  
options for
REPL (read–evaluate–print loop)
response mappings
restricted Boltzmann machines.
    See RBMs.
retrieval-based chatbots.
    See also search-based chatbots.
return_sequences keyword argument2nd3rd4th
return_state argument
RMSE (root mean square error)2nd3rd
RNNs (recurrent neural networks)2nd3rd
  
backpropagation
  
compiling models
  
hyperparameters
  
limitations of
  
predicting
    bidirectional recurrent neural networks
    statefulness
  
training models
  
updating
  
with Keras
robot journalists
RocketML pipelines
root conda environment
root mean square error.
    See RMSE.
row vectors
RSMProp
rstr package
rule-based algorithm

S

S matrix
SAD (sum of absolute distance)
saved models, loading
scalability
scalable vector graphics.
    See SVG.
scalar product
scaling datasets
scanners
scikit-learn package2nd3rd
scikit-learn TruncatedSVD transformer
scoring function
scoring topics, algorithms for
  
LDA
  
LDiA
search engines
  
algorithms
  
distributed
  
less manipulative
  
manipulative
  
open source
  
open source full-text indexers
search-based chatbots
  
context
  
example of
seed argument
segment_sentences() function
segmentation2nd
semantic analysis
  
algorithms for scoring topics
    LDA
    LDiA
  
distance
  LDA
    
classifiers
    LDiA and
  
LDiA
    as spam classifier
    for SMS messages
    history of
    LSA vs.
  
LSA
  
PCA
    for SMS message semantic analysis
    LSA for spam classification
    NLP (natural language processing)
    on 3D vectors
    truncated SVD for SMS message semantic analysis
  
similarity
  
steering with feedback
  
SVD
    left singular vectors
    matrix orientation
    right singular vectors
    S matrix
    singular values
    truncating topics
    U matrix
    VT matrix
  
TF-IDF vectors and lemmatization
  
topic vectors2nd
semantic queries
semantic searches2nd3rd
semicolons
sentence segmenting
sentiment
  
Naive Bayes model
  
rule-based sentiment analyzers
SentimentIntensityAnalyzer.lexicon
separable data
seq2seq (sequence-to-sequence networks)
sequence decoders
sequence encoders2nd

sequence-to-sequence
  
applications for
  
assembling networks
  
assembling pipelines
    sequence encoders
    thought decoders
  
building chatbots
    assembling models for sequence generation
    building character dictionary
    conversing with chatbots
    generating one-hot encoded training sets
    generating responses
    predicting sequences
    preparing corpus for training
    training sequence-to-sequence chatbots
  
conversations
  
encoder-decoder architecture
    autoencoders
    decoding thought
  
models in Keras
  
preparing datasets training
  
training enhancements
    attention mechanism
    bucketing
  
training networks

sequences
  
assembling models for sequence generation
  
generating output sequences
  
predicting
Sequential class
sequential minimal optimization.
    See SMO.
Sequential() class
Series object
SGD (stochastic gradient descent)2nd3rd
.shape attribute
shell commands
sigmoid activation function
  
fit method
  
optimizers
sigmoid function2nd
similarity2nd
simple_preprocess utility
simpleNumericalFactChecker
SimpleRNN layer2nd3rd4th
single-quotes
singular value decomposition.
    See SVD.
singular values
Skflow
skip-grams2nd3rd
sklearn MinMaxScaler
sklearn module2nd3rd4th
sklearn.decomposition.PCA
sklearn.LatentDirichletAllocation
sklearn.linear_model.Ridge regressor
sklearn.manifold.TSNE
sklearn.metrics.pairwise module
sklearn.PCA model2nd
sklearn.Pipeline object
sklearn.StandardScaler transform
sklearn.TruncatedSVD
Slack channels
SMO (sequential minimal optimization)
SMS messages
  
LDiA for
  semantic analysis
    
PCA for
    truncated SVD for
Snappy
Snowball stemmer

softmax function
  
learning vector representations
  
overview of
  
retrieving word vectors with linear algebra
softmax layer2nd
softmax output value
software patterns
sorted() method
spaces
SpaCy package2nd3rd4th5th6th7th
spacy.displacy
spacy.matcher.Matcher
spam classification2nd
spam dataset, SMS
spam filters
spam SMS messages
sparse continuous vectors
sparse matrices
speech recognition
speech to text.
    See STT.
Sphinx Search
split method
split_turns function
splitting2nd
spoofing
square brackets2nd
squared Euclidean distance
squashed vectors
<srai> tag
SSD (sum of squares distance)
ssh credentials
stacked layers
stacking LSTM layers
Stanford Core NLP library2nd
stanford-corenlp interface
star character2nd
start token
stateful keyword argument
statefulness
steering feature
steering with feedback
stemming2nd
  
algorithms for
  
process of
step function
step size
steps_per_epoch method
stochastic gradient descent.
    See SGD.
STOP token
stop words
stop_condition2nd
str class
str.lower() function
str.split() method2nd3rd
stride
string buffer
string manipulation
strings
strip method
STT (speech to text)
style conventions
Stylometry
subject
subsampling frequent tokens
sum of absolute distance.
    See SAD.
sum of squares distance.
    See SSD.
.sum() method
supervised algorithms
SVD (singular value decomposition)2nd3rd4th
  
enhancements to
  
left singular vectors
  
matrix orientation
  
right singular vectors
  
S matrix
  
singular values
  
truncated
  
truncating topics
  
U matrix
  
VT matrix
SVG (scalable vector graphics)
SVM (support vector machine)
symmetry property
synonyms
SyntaxNet package2nd

T

T attribute
t-Distributed Stochastic Neighbor Embedding (t-SNE)2nd
tanh activation function
target labels
target (output) variable
target unzipper
target values
target words
target_seq2nd
target_text
tdm term-document matrix
teacher forcing method
templates in Python
tensor processing units.
    See TPUs.
TensorBoard2nd
TensorFlow2nd3rd4th5th6th
term frequency.
    See TF.
term frequency times inverse document frequency.
    See TF-IDF.
term frequency vectors
term-document matrices
term-topic matrix
terminals
test set
text characters
text retrieval
TF (term frequency)
TF vectors
TF-IDF (term frequency times inverse document frequency)2nd
  
bag of words
  
lemmatization and vectors
  
topic modeling
    alternatives to
    Okapi BM25
    relevance ranking
    tools for
    Zipf‘s Law
  
vectorizing
  
Zipf‘s Law
tfidf vector
TfidfVectorizer
TFIDFVectorizer model2nd
Theano2nd3rd
therapy chatbots
thinking, high-dimensional
  
1D index
  
2D, 3D, 4D indexes
thought decoders2nd
thought encoders
threshold function2nd
time series data
Time Series Matching
time step
tmp directory
.todense() method
token step
token-by-token prediction
tokenized phrases
tokenizers
  
dot products
  
measuring bag-of-words overlap
  n-grams
    
overview of
    stop words
  
normalizing vocabulary
    case folding
    lemmatization
    stemming
    use cases
  
regular expressions
    overview of
    separating contractions
    separating words
tokens2nd
  
morphology of
  
subsampling
topic modeling
  
alternatives to
  
Okapi BM25
  
relevance ranking
  
tools for
  
Zipf‘s Law
topic vectors2nd
topic weights
topics2nd
topn argument
Torch package
TPUs (tensor processing units)
train_test_split() method
train/ directory
trained model2nd

training
  
CNNs
  
document vectors
  
domain-specific Word2vec models
  
enhancements for
    attention mechanism
    bucketing
  
models2nd3rd
    on GPUs
    reducing memory footprint during training
  
perceptrons
  
preparing corpus for
  
preparing datsets for sequence-to-sequence training
  
sequence-to-sequence chatbots
  
sequence-to-sequence networks
training data, loading
training methods
training samples
training sets2nd
  
imbalanced
    augmenting data
    oversampling
    undersampling
training_set_generator function
transfer learning
.transform() method
transpose of matrix
Treebank tokenizer
Treebank Word Tokenizer2nd3rd
triangle inequality property
trigger words
trigrams
triple-quoted raw strings
troll message filtering
truncated characters
truncated data
truncated singular value decomposition
TruncatedSVD model2nd
truncating topics in SVD
TurboTax, Intuit
Turing test
tutorials
two-dimensional filter shape

U

U matrix
Ubuntu Dialog Corpus2nd
Ubuntu package manager
UI (user interface)
underfitting2nd
undersampling
underscore character2nd
Unicode characters
unique tokens2nd
units2nd
UNK (unknown) token
unnatural words
unrolled net2nd3rd4th
update gate
updating RNNs
user interface.
    See UI.
UX (user experience)

V

VADER algorithm
vaderSentiment package
validating dates
validation set
vanishing gradient problem
variance, maximizing
variational autoencoder
vector difference
vector dimensions
vector representations
vector space model.
    See VSM.
vector spaces2nd
  
indexes
  
splitting
vector-oriented reasoning
vectorizing
vectors2nd
  
3D vectors
  
distances
    cosine distance
    Euclidean distance
    Manhattan distance
  
high-dimensional
    hashes
    high-dimensional thinking
    vector space indexes
  
left singular
  
right singular
virtual assistant chatbots
virtual assistants
virtual private cloud.
    See VPC.
VirtualBox application
visualize_embeddings function
vocabulary, normalizing
  
case folding
  
lemmatization
  
stemming
  
use cases
VPC (virtual private cloud)
VSM (vector space model)2nd3rd
VT matrix

W

Watson Semantic Web Search
WebSphinx search engine
weights2nd3rd4th5th6th
whitespace2nd
Whoosh, Python
Wikia Search
Wikidata
Will chatbot framework
  
installing
  
untrained
word embeddings2nd
word frequencies
word order2nd
word prediction
word proximity

word tokenization
  
challenges of
  
sentiment
    Naive Bayes model
    rule-based sentiment analyzers
  
tokenizers
    dot products
    measuring bag-of-words overlap
    n-grams
    normalizing vocabulary
    regular expressions
word vectors
  
analogies
  
computing Word2vec representations
    CBOW approach
    frequent bigrams
    negative sampling
    skip-gram approach
    skip-gram vs. CBOW
    softmax function
    subsampling frequent tokens
  
documenting similarity with Doc2vec
  
embedding
  
fastText
  
generating word vector representations
    preprocessing steps
    training domain-specific Word2vec models
  
gensim.Word2vec modules
  
retrieving with linear algebra
  
semantic queries
  
unnatural words
  
vector-oriented reasoning
  
visualizing word relationships
word-topic vectors
Word2vec2nd3rd4th5th
  
computing representations
    CBOW approach
    frequent bigrams
    negative sampling
    skip-gram approach
    skip-gram vs. CBOW
    softmax function
    subsampling frequent tokens
  
gensim.Word2vec modules
  
GloVe vs.
  
LSA vs.
  
training domain-specific models
WordNetLemmatizer

words
  
order of
  
separating
.words() method
Wysa chatbot

X

XOR problem

Y

Yacy search engine
YAGO
Yandex
Your Dictionary
YourDOST

Z

zero determinant
zero vector2nd
Zettair indexer
zeugma
Zipf‘s Law2nd