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
  • adding / Adding 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)
  • about / Convolutional Neural Networks (CNNs) in Learn

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
  • about / The multiple hidden layer model, 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
  • about / Single hidden layer model, Single hidden layer explained
  • exploring / Exploring the single hidden layer model
• single hidden layer neural network model
  • about / The 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