[SYMBOL][A][B][C][D][E][F][G][H][I][J][K][L][M][N][O][P][Q][R][S][T][U][V][W][Y]
+ (plus symbol)
1D (one-dimensional) tensor
500-by-500 tensor
accuracy
ACTION_DROP, responding to
actions
activation function
AdamOptimizer
ADC (analog-to-digital converter)
add_summary method
amounts vector
arbitrary functions
artificial neural network
AUC (area-under-curve) value
audio, extracting features from
augment data
autoencoders, 2nd, 3rd
applying
batch training
neural networks
working with images
automatic differentiation capabilities
autonomous agent
BasicLSTMCell
batch learning
batch training
Bayesian networks
best-fit curve, 2nd, 3rd
bias
binary classifier, 2nd
black box
BMU (best matching unit)
Boolean variables, Python
BregmanToolkit
cells, evaluating
centroid
chatbots, seq2seq models
architecture of
classifications
gathering dialogue data
RNNs
vector representation of symbols
chromagram
CIFAR-10 web page
cifar_tools.py file
classification
formal notation for
measuring performance of classification algorithm
accuracy
precision and recall
ROC curve
multivariate classification
one-versus-all approach for
one-versus-one approach for
softmax regression for
overview
real-world application of
using linear regression for
using logistic regression for
one-dimensional logistic regression
two-dimensional logistic regression
classifiers, training
clustering, 2nd
extracting features from audio
k-means clustering
loading files in TensorFlow
real-world application of
segmentation
SOM
CNNs (convolutional neural networks), 2nd
code
performance, trouble-shooting
visualizing as graph
writing in Jupyter
configurations, session
confusion matrix, 2nd
constant value
contextual information
continuous outputs
conv2d function, 2nd
convex
convolution layer
convolutional neural networks
applying
implementing in TensorFlow
measuring performance
training classifiers
improving performance of
neural networks, disadvantages of
overview
preparing images
convolving using filters
generating filters
max-pooling
convolving images, using filters
Cornell Movie Dialogue corpus
correct_prediction
correspondence problem
cost function, 2nd, 3rd, 4th, 5th, 6th
cross-entropy loss
CSV datasets
curr_value
curse of dimensionality
data
cleaning
clustering
extracting features from audio
k-means clustering
loading files in TensorFlow
real-world application of
segmentation
SOM
dialogue, gathering
encoding
higher-dimensional
linear fit
loading
overfitting, 2nd
underfitting
visualizing using TensorBoard
implementing moving averages
visualizing moving averages.
See time-series data.
data representation, 2nd
data split
dataflow graph, 2nd
Data.gov
data_loader.py file
datasets, CSV
deciding boundary
decoder_input_embedded
decoder_input_seq, 2nd
decoding
deeper architecture
DeepRL
denoising autoencoder
dependent variables
dimensionality reduction
discount factor
discrete Fourier transform
discrete outputs
distance metrics
DistBelief
Docker tool
installing on Linux
installing on macOS
installing on Windows
to install TensorFlow
using
docker-machine ip command
dot product, 2nd
dtype (data type)
dummy data, 2nd
dummy variables
early stopping
edges, 2nd
E-M algorithm
embedding images
embedding_lookup function
embed_sequence function
emission matrix, 2nd
emission probability matrix
encoder_state variable
encoding
<EOS> (end-of-sequence) symbol
epoch
epsilon parameter
Euclidian distance
eval() function
executing a forward step
exploration vs. exploitation, 2nd
face detection and pose estimation, databases for
fast Fourier transform
FDDB (Face Detection Data Set and Benchmark)
feature engineering, 2nd
feature vector, 2nd, 3rd
features
distance metrics
extracting from audio
overview
feed_dict argument
files, loading in TensorFlow
filters
convolving images using
generating
FN (false negative)
forward algorithm
Fourier transform
FP (false positive)
freq variable
frequency domain
Frossard, Davi
future time-steps
game playing
GAN (generative adversarial networks)
Gaussian distribution, 2nd
generating filters
genetic algorithms
global_variables_initializer op
<GO> symbol
go_prefixes matrix
gradient clipping
gradient descent
graphs, visualizing code as
greedy policy
Hardware-Assisted Virtualization Detection Tool, Microsoft
helper functions
hidden layers, 2nd
hidden neurons, 2nd
HMMs (hidden Markov models)
forward algorithm
Markov model
modeling DNA
modeling images
modeling video
not-so-interpretable models
overview of
real-world application of
Viterbi decoding algorithm
hyperbolic tangent function
hyperparameters, 2nd
identity
ImageNet dataset
images
embedding
loading
preparing
convolving using filters
generating filters
max-pooling
ranking
working with
img placeholder
imitation learning
independent variables
inductive learning
inference
inference_logits op
initial probabilities
initial probability vector
input_dim
installing
Matplotlib
TensorFlow by using Docker
installing Docker on Linux
installing Docker on macOS
installing Docker on Windows
using Docker
int32 type
interactive session mode
interpretable models
jerk
Jupyter Notebook, writing code in
Kaggle
k-means algorithm
k-means clustering
L0 norm
L1 norm
L2 norm
Large Movie Review Dataset
libraries, testing
LibriSpeech
linear decision boundary
linear functions
linear model
linear regression
for classification
formal notation for
polynomial model
real-world application of
regularization
simple regression model
solving
L-infinity norm
Linux operating system, installing Docker on
L-N norm
loading variables
load_sentences function
load_series function
logistic regression, for classification
one-dimensional
two-dimensional
LSTM (Long Short-Term Memory)
LSTMCell class
machine learning
fundamentals of
inference
learning
parameters
types of
reinforcement learning
supervised learning
unsupervised learning
macOS (operating system), installing Docker on
main function
make_cell function
Manhattan distance
maps, SOM
Markov model
Matplotlib library, installing
matrix, 2nd
max function, 2nd
max-pooling technique, 2nd
MDP (Markov decision process)
metaparameter
Moby
model, 2nd, 3rd
moving averages
implementing
visualizing
multiclass classifier
MultiRNNCell
multivariate classification
one-versus-all approach for
one-versus-one approach for
softmax regression for
multivariate regression
name scopes
negative operator, 2nd
negative value
neural networks
applying, 2nd
contextual information
disadvantages of
implementing
implementing in TensorFlow
measuring performance
training classifiers
improving performance of
overview, 2nd
predictive model for time-series data
preparing images
convolving using filters
generating filters
max-pooling
NLP (natural language processing), 2nd
nodes, 2nd
nominal values
nonconvex function
nonlinear functions
normal distribution, 2nd
num_iters
NumPy array, 2nd
one-dimensional (1D) tensor
one-hot encoding, 2nd, 3rd
one-versus-all approach, for multivariate classification
one-versus-one approach, for multivariate classification
operations, defining
operators
creating
executing with sessions
setting session configurations
visualizing code as graph
optimal point
optimal policy
ordinal type, values
outliers
output layers, 2nd
overfitting, 2nd
parameters, 2nd
parameter-space
PCA (principle component analysis)
pixel intensity
placeholders, 2nd, 3rd
plus symbol (+)
policies, in RL
polynomial regression model
precision
predictive models, for time-series data
preference models
probability matrix
Q-function, 2nd, 3rd
Q-learning decision policy
ramp (ReLU) function
random policy
ranking, images
reader.read function
recall
receiver operating characteristic curve.
See ROC curve.
recentering
recurrent neural networks.
See RNNs.
regression
for classification
one-dimensional
two-dimensional
for multivariate classification
formal notation for
multivariate
polynomial model
real-world application of
regularization
simple regression model
solving
regressor
regularization
regularizing weights
relu function
representing tensors
restore function
rewards
RL (reinforcement learning)
applying, 2nd
implementing
overview
policies
utilities
rnn.dynamic_rnn function
RNNs (recurrent neural networks), 2nd
applying
contextual information
implementing
overview
predictive model for time-series data
robotics
ROC (receiver operating characteristic) curve
row-major order
saving variables
scikit-learn library
SciPy
segmentation
segment_size
select_action method
self.fwd
self.obs_idx
self-organizing map.
See SOM.
sentences
Sentiment Labelled Sentences Data Set
seq2seq (sequence-to-sequence) models
architecture of
classifications
gathering dialogue data
RNNs
vector representation of symbols
sequence_loss method
SeriesPredictor class, 2nd
Session context
session.run() statement
sessions
executing operators with
setting session configura-tions
visualizing code as graph
logging
show_weights function
sig (sigmoid) function
sigmoids
simple_regression.py file
simulated annealing
slice function
softmax function, 2nd
softmax_cross_entropy_with _logits function
softmax.py file
SOM (self-organizing map)
split_data function
stacked autoencoder, 2nd
stride length, 2nd
SummaryWriter
supervised learning
symbols, vector representation of
tb_files
TED-LIUM
temporal dependencies
Tensor class
TensorBoard dashboard, visualizing data using
implementing moving averages
visualizing moving averages
TensorFlow library
implementing convolutional neural networks in
measuring performance
training classifiers
installing by using Docker
installing Docker on Linux
installing Docker on macOS
installing Docker on Windows
using Docker
loading files in
testing
tensors, representing
test function, 2nd
testing sets
testing TensorFlow
tf.add(x, y) operator
tf.concat operator
tf.constant operator
tf.convert_to_tensor( ... ) function
tf.div(x, y) operator
tf.exp(x) operator
tf.floordiv(x, y) operator
tf.InteractiveSession() function
tf.mod(x, y) operator
tf.multiply(x, y) operator
tf.nn.dropout function
tf.nn.dynamic_rnn function
tf.nn.softmax_cross_entropy _with_logits library
tf.pow(x, y) operator
tf.Session() function
tf.sqrt(x) operator
tf.subtract(x, y) operator
tf.train.match_filenames_once operator
tf.train.string_input_producer operator
tf.truediv(x, y) operator
three-dimensional vector
time-series data, predictive model for
Tinker
TN (true negative)
TP (true positive)
train method, 2nd
training classifiers
training dataset, 2nd
TrainingHelper
train_op, 2nd, 3rd
transition matrix, 2nd
trellis diagram
Twitter Sentiment Analysis Dataset
two-input network
underfitting
unpickle function
unsupervised learning
update_avg operator
utilities, in RL
utility functions
embedding images
preference models
ranking images
validation
value of state
Variable class
variables
loading
saving
using
variance
variational autoencoder
vector representations, of symbols
vectors
VGG Face Dataset
viterbi cache
Viterbi decoding algorithm
VoxForge
Windows operating system, installing Docker on
WolframAlpha
18.223.108.119