Index
A
- activation functions / Sigmoid function
B
- backpropagation / Backpropagation
- basic neural networks
- about / Basic neural networks
- log function / Log function
- sigmoid function / Sigmoid function
C
- CoCalc
- TensorFlow, installing via / Installing via CoCalc
- reference / Installing via CoCalc
- computations
- defining / Simple computations
- scalars, defining / Defining scalars and tensors
- tensors, defining / Defining scalars and tensors
- on tensors / Computations on tensors
- performing / Doing computation
- intermediate values, viewing / Viewing and substituting intermediate values
- intermediate values, submitting / Viewing and substituting intermediate values
- convolutional and pooling layer combo
- convolutional layer
- about / Convolutional layer motivation
- multiple features, extracting / Multiple features extracted
- convolutional layer application
- implementing / Convolutional layer application
- about / Exploring the convolution layer
- convolutional neural network
- about / Convolutional neural network
- Convolutional Neural Network (CNN)
- fonts, classifying / CNN to classify our fonts
- Convolutional Neural Networks (CNNs)
D
- deep CNN
- about / Deep CNN
- convolutional and pooling layer combo, adding / Adding convolutional and pooling layer combo
- deep convolutional neural network / Deep convolutional neural network
- deeper CNN
- layers, adding / Adding a layer to another layer of CNN
- conclusion / Wrapping up deep CNN
- deep neural network
- Dense Neural Network (DNN)
- about / DNNs
- CNNs / Convolutional Neural Networks (CNNs) in Learn
- weights, extracting / Extracting weights
E
- epoch / Training the model
F
- font classification dataset / Introducing the font classification dataset
H
- hyper parameter optimization / Exploring the multiple hidden layer model
I
- installation page, TensorFlow / TensorFlow – the installation page
J
- Jupyter / Installing via CoCalc
K
- Keras
- URL / TensorFlow learn
L
- log function / Log function
- logistic regression / Logistic regression, Logistic regression
- about / Logistic regression model building
- implementing / Getting data ready
- logistic regression model
- weights, viewing of / Understanding weights of the model
- about / The logistic regression model
- logistic regression model building
- about / Logistic regression model building
- font classification dataset / Introducing the font classification dataset
- logistic regression training
- about / Logistic regression training
- loss function, developing / Developing the loss function
- model, training / Training the model
- model accuracy, evaluating / Evaluating the model accuracy
M
- main page, TensorFlow / TensorFlow – main page
- models
- about / A quick review of all the models
- logistic regression model / The logistic regression model
- single hidden layer neural network model / The single hidden layer neural network model
- deep neural network / Deep neural network
- convolutional neural network / Convolutional neural network
- deep convolutional neural network / Deep convolutional neural network
- multiple hidden layer model
- about / The multiple hidden layer model
- exploring / Exploring the multiple hidden layer model
- results / Results of the multiple hidden layer
- multiple hidden layers graph / Understanding the multiple hidden layers graph
N
- neuron / Sigmoid function
P
- pip
- TensorFlow, installing via / Installing via pip
- pooling layer application
- about / Pooling layer application
- pooling layers
- about / Pooling layer motivation
- max pooling layers / Max pooling layers
R
- Recurrent Neural Networks (RNNs)
- about / Exploring RNNs, Understanding RNNs
- weights, modeling / Modeling the weights
- using / Modeling the weights
- research evaluation
- about / Research evaluation
S
- same padding
- about / Convolutional layer motivation
- scalars
- defining / Defining scalars and tensors
- sigmoid function / Sigmoid function
- single hidden layer model
- single hidden layer neural network model
- Stochastic Gradient Descent (SGD)
- about / DNNs
T
- TensorFlow
- installing / Installing TensorFlow
- main page / TensorFlow – main page
- installation page / TensorFlow – the installation page
- installing, via pip / Installing via pip
- installing, via CoCalc / Installing via CoCalc
- future / The future of TensorFlow
- projects / Some more TensorFlow projects
- TensorFlow learn
- about / TensorFlow learn
- reference link / TensorFlow learn
- setup / Setup
- logistic regression / Logistic regression
- TensorFlow model
- building / Building a TensorFlow model
- TensorFlow Slim
- reference link / TensorFlow learn
- tensors
- defining / Defining scalars and tensors
- computations on / Computations on tensors
V
- variable tensors / Variable tensors
W
- weights
- modeling / Modeling the weights
- extracting / Extracting weights
- wheel file / TensorFlow – the installation page