Index
A
- activation functions
- implementing / Getting ready, How to do it…, How it works…, There's more…
- used, for implementing gates / Working with Gates and Activation Functions, How to do it…, How it works…, There's more…
- sigmoid / Getting ready
- rectified linear unit (ReLU) / Getting ready
- address matching example
- advanced CNN
- implementing / Implementing an Advanced CNN, Getting ready, How to do it…, How it works…
- Amazon Web Services (AWS)
- about / There's more…
- Amherst statistical dataset repository
- URL / Getting ready
- Anaconda package
- URL / Getting ready
- Arxiv.org
- about / There's more…
- AWS machine images (AMIs)
- about / There's more…
B
- back propagation
- implementing / Implementing Back Propagation, Getting ready, How to do it…, There's more…
- advantages / There's more…
- usage / There's more…
- disadvantages / There's more…
- bag of words
- implementing / Working with bag of words, How to do it…, There's more…
- batch training
- implementing / Working with Batch and Stochastic Training, How to do it…, How it works…
- advantages / There's more…
- disadvantages / There's more…
- bilingual sentence dataset
- URL / There's more…
- birth weight data
- about / How to do it…
- Boston Housing data
- about / How to do it…
C
- CIFAR-10 dataset
- URL / See also
- CIFAR-10 image data
- about / How to do it…
- URL / How to do it…
- CIFAR10 dataset
- URL / Getting ready
- classifier
- creating, on iris dataset / Combining Everything Together, Getting ready, How to do it…, There's more…
- clustering
- with k-means / Clustering Using K-Means, How to do it…, There's more…
- command line
- graphs, visualizing / There's more…
- computational graph
- operations, creating / Operations in a Computational Graph, How to do it…
- Continuous bag of words (CBOW) method
- about / Getting ready
- implementing / Working with CBOW Embeddings, Getting ready, How to do it…, How it works…
- convolution
- about / Introduction
- Convolutional Neural Networks (CNNs)
- about / Introduction
- resources / See also
- cross entropy
- about / How to do it
D
- data sources
- about / Working with Data Sources
- using / Getting ready, How to do it…, How it works…
- iris data / How to do it…
- birth weight data / How to do it…
- Boston Housing data / How to do it…
- MNIST handwriting data / How to do it…
- spam-ham text data / How to do it…
- movie review data / How to do it…
- CIFAR-10 image data / How to do it…
- works of Shakespeare text data / How to do it…
- English-German sentence translation data / How to do it…
- decoding
- about / Introduction
- decomposition method
- implementing / Implementing a Decomposition Method, How to do it…, How it works…
- Deepdream
- implementing / Implementing DeepDream, How to do it…, There's more…
- references / See also
- Deming regression
- implementing / Implementing Deming regression, Getting ready, How to do it…, How it works…
- Doc2vec
- used, for sentiment analysis / Using Doc2vec for Sentiment Analysis, Getting ready, How to do it…, How it works…
E
- elastic net regression
- implementing / Implementing Elastic Net Regression, How to do it…
- encoding
- about / Introduction
- English-German sentence translation data
- about / How to do it…
- epoch
- about / There's more…
- existing CNNs models
- retraining / Retraining Existing CNNs models, Getting ready, How to do it…
- Exponential Linear Unit (ELU)
- about / How to do it…
G
- gates
- implementing, with activation functions / Working with Gates and Activation Functions, How to do it…, How it works…, There's more…
- genetic algorithm
- implementing / Working with a Genetic Algorithm, Getting ready, How to do it…, How it works…, There's more…
- graphs
- visualizing, in Tensorboard / Visualizing graphs in Tensorboard, Getting ready, How to do it…
- visualizing, from command line / There's more…
H
- Hello World program
- writing, for image recognition / Getting ready
I
- image recognition
- with nearest neighbors / Getting ready, How to do it…, How it works…
- Inception
- about / Getting ready
- reference link / See also
- iris data
- about / How to do it…
- iris dataset
- classifier, creating / Combining Everything Together, Getting ready, How to do it…, There's more…
- references / See also
K
- k-means
- used, for clustering / Clustering Using K-Means, How to do it…, There's more…
- k-nearest neighbors (k-NN)
- about / Introduction
- Kernels
- implementing, in TensorFlow / Working with Kernels in TensorFlow, Getting ready, How to do it…, How it works…, There's more…
L
- lasso regression
- implementing / Implementing Lasso and Ridge Regression, How to do it…, How it works…
- Levenshtein distance / Getting ready
- linear models
- predictions, improving / Improving the Predictions of Linear Models, How to do it, How it works…
- linear regression
- about / Introduction
- in TensorFlow / Getting ready, How to do it…, How it works…
- loss functions, implementing / Understanding Loss Functions in Linear Regression, How to do it…, How it works…
- implementing / Reduction to Linear Regression, Getting ready, How to do it…, How it works…
- linear separator
- defining / Introduction
- linear SVM
- implementing / Working with a Linear SVM, How to do it…, How it works…
- logistic regression
- implementing / Implementing Logistic Regression, How to do it…, How it works…
- Long Short Term Memory (LSTM)
- about / Introduction, Getting ready
- loss functions
- implementing / Implementing Loss Functions, How to do it…, How it works…, There's more…
- usage / There's more…
- benefits / There's more…
- disadvantages / There's more…
- in linear regression / Understanding Loss Functions in Linear Regression, How to do it…, How it works…
- LSTM model
- implementing / Implementing an LSTM Model, Getting ready, How to do it…, How it works…
M
- ManyThings.org
- URL / How to do it…
- math functions
- abs() / How to do it…
- ceil() / How to do it…
- cos() / How to do it…
- exp() / How to do it…
- floor() / How to do it…
- inv() / How to do it…
- log() / How to do it…
- maximum() / How to do it…
- minimum() / How to do it…
- neg() / How to do it…
- pow() / How to do it…
- round() / How to do it…
- rsqrt() / How to do it…
- sign() / How to do it…
- sin() / How to do it…
- sqrt() / How to do it…
- square() / How to do it…
- matrices
- about / Working with Matrices
- creating / How to do it…
- matrix inverse method
- mean squared error (MSE) / How to do it…
- mixed distance functions
- computing with / Computing with Mixed Distance Functions, Getting ready, How to do it…, How it works…, There's more…
- MNIST (Mixed National Institute of Standards and Technology) / How to do it…
- MNIST handwriting data
- about / How to do it…
- URL / How to do it…
- model metric
- R-squared (coefficient of determination) / There's more…
- RMSE (root mean squared error) / There's more…
- Confusion matrix / There's more…
- Recall / There's more…
- Precision / There's more…
- F-score / There's more…
- models
- evaluating / Evaluating Models, How it works…
- movie review data
- about / How to do it…
- URL / How to do it…, Getting ready
- multi-class SVM
- implementing / Implementing a Multi-Class SVM, Getting ready, How to do it…, How it works…
- multilayer neural network
- implementing / Using a Multilayer Neural Network, How to do it…
- multiple executors
- multiple layers
- connecting / Working with Multiple Layers, How to do it…, How it works…
- multiple LSTM Layers
- stacking / Stacking multiple LSTM Layers, Getting ready, How to do it…, How it works…
N
- nearest neighbors
- implementing / Working with Nearest Neighbors, Getting ready, How to do it…, How it works…
- used, for image recognition / Getting ready, How to do it…, How it works…
- reference link / There's more…
- nested operations
- layering / Layering Nested Operations, How to do it…, There's more…
- neural network (nn) library
- about / How to do it…
- neural networks
- about / Introduction
- resources / Introduction
- different layers, implementing / Implementing Different Layers, Getting ready, How to do it…, How it works…
- non-linear SVM
- implementing / Implementing a Non-Linear SVM, How to do it…
- Nvidia Cuda Toolkit
- URL / Getting ready
O
- one layer neural network
- implementing / Implementing a One-Layer Neural Network, How to do it…, There's more…
- operational gates
- implementing / Implementing Operational Gates, How to do it…, How it works…
- operations
- declaring / Declaring Operations, How to do it…, There's more…
- creating, in computational graph / Operations in a Computational Graph, How to do it…
- nested operations, layering / Layering Nested Operations, How it works…, There's more…
- ordinary differential equations (ODEs)
- about / Getting ready
P
- placeholders
- predictions
- estimating, with Word2vec / Making Predictions with Word2vec, How to do it…, How it works…, There's more…
- Python 3.4+
- URL / Getting ready
- Python Image Library (PIL) / How to do it…
R
- rectified linear unit (ReLU)
- about / Getting ready
- advantages / How it works…
- disadvantages / How it works…
- recurrent neural network (RNN)
- about / Introduction
- implementing, for spam prediction / Implementing RNN for Spam Prediction, Getting ready, How to do it…, There's more…
- ridge regression
- implementing / Implementing Lasso and Ridge Regression, How to do it…, How it works…
S
- sentiment analysis
- with Doc2vec / Using Doc2vec for Sentiment Analysis, How to do it…, How it works…
- sequence-to-sequence models
- creating / Creating Sequence-to-Sequence Models, How to do it…, How it works…
- creating / How to do it…
- Siamese similarity measure
- sigmoid
- about / Getting ready
- advantages / How it works…
- disadvantages / How it works…
- simpler CNN
- implementing / Implementing a Simpler CNN, How to do it…, How it works…
- skip-gram embedding
- implementing / Working with Skip-gram Embeddings, Getting ready, How to do it…, How it works…
- spam-ham phone text database
- URL / Getting ready
- spam-ham text data
- about / How to do it…
- spam prediction
- recurrent neural network (RNN), implementing / Implementing RNN for Spam Prediction, Getting ready, How to do it…, There's more…
- specialty mathematical functions
- about / How to do it…
- digamma() / How to do it…
- erf() / How to do it…
- erfc() / How to do it…
- igamma() / How to do it…
- igammac() / How to do it…
- lbeta() / How to do it…
- lgamma() / How to do it…
- squared_difference() / How to do it…
- stochastic training
- implementing / Working with Batch and Stochastic Training, How to do it…, How it works…
- advantages / There's more…
- disadvantages / There's more…
- Stylenet/Neural-Style
- system of ODEs
- solving / Solving a System of ODEs, How to do it…
T
- Tatoeba Project
- URL / Getting ready
- Tatoeba project
- URL / How to do it…
- Tensorboard
- graphs, visualizing / Visualizing graphs in Tensorboard, Getting ready, How to do it…
- graphs, visualizing from command line / There's more…
- TensorFlow
- computation / How TensorFlow Works, How it works…
- tutorials, URL / See also
- resources / Additional Resources
- resources, URL / How to do it…
- linear regression, implementing / Getting ready, How to do it…, How it works…
- Kernels, implementing / Working with Kernels in TensorFlow, Getting ready, How to do it…, How it works…, There's more…
- GPU version, URL / Getting ready
- parallelizing / Parallelizing TensorFlow, How to do it…
- best practices / Taking TensorFlow to Production, How to do it…, How it works…
- productionalizing, with example / Productionalizing TensorFlow – An Example, How to do it…
- TensorFlow 0.12
- URL / Getting ready
- TensorFlow Distributed / Getting ready
- TensorFlow Python API
- URL / See also
- tensors
- about / Declaring Tensors
- declaring / Declaring Tensors, How to do it…, How it works…, There's more…
- text based distances
- measuring / Getting ready, How to do it…, How it works…
- Hamming distance / There's more…
- Cosine distance / There's more…
- Jaccard distance / There's more…
- Text Frequency - Inverse Document Frequency (TF-IDF)
- implementing / Implementing TF-IDF, Getting ready, How to do it…, How it works…
- about / Getting ready
- Tic Tac Toe
- moves, learning / Learning to Play Tic Tac Toe, Getting ready, How to do it…, How it works…
- reference link / Getting ready
U
- unit tests
- implementing / Implementing unit tests, Getting ready
- reference link / How it works…
V
- variables
W
- Word2vec
- about / Getting ready
- used, for estimating predictions / Making Predictions with Word2vec, How to do it…, How it works…, There's more…
- workflow, TensorFlow
- dataset, importing / How to do it…
- dataset, generating / How to do it…
- data, normalizing / How to do it…
- data, transforming / How to do it…
- dataset, partitioning / How to do it…
- algorithm parameters, setting / How to do it…
- variables, initializing / How to do it…
- placeholders, initializing / How to do it…
- model structure, defining / How to do it…
- loss functions, declaring / How to do it…
- model, initializing / How to do it…
- model, training / How to do it…
- model, evaluating / How to do it…
- hyperparameters, tuning / How to do it…
- outcomes, deploying / How to do it…
- outcomes, predicting / How to do it…
- works of Shakespeare text data
- about / How to do it…