Index
A
Artificial intelligence (AI)
Artificial neural networks (ANNs)
B
Backward propagation
Bagging technique
Boosted trees method
ensemble methods
bagging technique
boosting technique
gradient boosting
Boosting technique
C
Colaboratory
ConvNets
SeeConvolutional neural networks (CNNs
Convolutional neural networks (CNNs)
architectures
DenseNet
GoogleNet
ResNet
VGG-16
convolutional layer
definition
dot product
feature map
grayscale image
ReLU function
fully connected layer
pooling layers
ReLU activation function
TensorFlow 2.0
D, E
Databricks
account and spin up
clusters
libraries options
notebook
PyPI source
TensorFlow package
Deep neural network (DNN)
hidden layers
Keras model
optimization function
output layers
test evaluation
training evaluation
DenseNet architecture
F
Forward propagation
Frequency-based techniques
G, H
GoogleNet architecture
Gradient boosting method
I, J, K
Image processing
CNNs
SeeConvolutional neural networks (CNNs
definition
transfer learning
variational autoencoders
applications of
architecture
autoencoders
implementation of
L
Linear regression
equation
graph
TensorFlow package
boston housing data set
correlation graph.
descriptive statistics
input pipeline
model training
modules
predictions
test split
validation
Logistic regression
correlation graph
definition
descriptive statistics
input pipeline
iris data set
model training
modules
multi-class equation
predictions
sigmoid function
test split
validation
M
Machine learning
python model deployment
flask
input web page
output HTML
REST
saving and restoring
user input’s HTML
Model deployment
application management
collaboration
definition
isolation
keras TensorFlow
load balancer
machine learning model
flask
REST service
saving and restoring
templates
performance
updates
N, O
Natural language processing (NLP)
bag-of-words approach
categories
curves
overview
TensorFlow projector
text classification
deep learning model
embeddings
libraries and reading
processing
text preprocessing
standard off-the-shelf methods
tokenization
word embeddings
Neural networks
architecture
artificial neural network
backward propagation
data set
definition
DNN
forward propagation
neurons
regression
P, Q
Pooling layers
Prediction-based techniques
R
Ranking tensors
Representational state transfer (REST)
ResNet architecture
S
Supervised machine learning
architecture
artificial intelligence types
definition
linear regression
equation
graph
keras and TensorFlow package
logistic regression
overfitting
testing/prediction
training phase
T, U
TensorFlow
components
definition
1.xvs.
2.x (Beta version)
categories of
documentation and data sources
eager execution
high-level APIs
keras
performance related changes
redundancy
session execution
simpler APIs
tf.function
usability related changes
operations
Anaconda
Colab
databricks
projector
data load
embeddings
visualization
tensor
computational graph
flow
properties
rank
shape
vector
coordinate system consideration
unit vectors
variable types
Transfer learning
definition
domain
machine learning
advantages
applications of
methodology
V, W, X, Y, Z
Variational autoencoders (VAE)
applications of
architecture
autoencoders
implementation of
batching and shuffling data
encoder and decoder
optimizer function
Python modules
trained model
training
VGG-16 architecture
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