Index

A

• activation functions
  • about / Activation functions
  • sigmoid / Sigmoid
  • hyperbolic tangent function / The hyperbolic tangent function
  • Rectified Linear Unit (ReLU) / The Rectified Linear Unit (ReLU)
• adjacency matrix / Label propagation
• Adjusted Rand Index / Adjusted Rand Index
• adversarial examples / Adversarial examples
• affine transformation / Spatial transformer networks
• affinity matrix / Label propagation
• AlexNet model / The AlexNet model
• algorithms
  • localizing / Localizing algorithms 
  • objects localizing, sliding windows used / Localizing objects using sliding windows
  • regression / Thinking about localization as a regression problem
• Amazon Web Services (AWS) / Development environment setup
• application areas, RNNs
  • Natural Language Modeling / Application areas of RNNs
  • Voice and Speech Recognition / Application areas of RNNs
  • Image/Video Description or Caption Generation / Application areas of RNNs
  • TimeSeries Data / Application areas of RNNs
• approaches, spectral clustering
  • k-Nearest Neighbors (KNN) / Spectral clustering
  • radial basis function (RBF) / Spectral clustering
• approximate nearest neighbor oh yeah (ANNOY)
  • about / Matching faster using approximate nearest neighbour
  • advantages / Advantages of ANNOY
• artificial neural network (ANN)
  • about / Understanding deep learning, Artificial neural network (ANN)
  • one-hot encoding / One-hot encoding
  • softmax / Softmax
  • cross-entropy / Cross-entropy
  • dropout / Dropout
  • batch normalization / Batch normalization
  • L1 regularization / L1 and L2 regularization
  • L2 regularization / L1 and L2 regularization
  • training / Training neural networks
  • backpropagation / Backpropagation
  • gradient descent / Gradient descent
  • stochastic gradient descent (SGD) / Stochastic gradient descent
• artificial neuron / The perceptron
• assumptions, semi-supervised model
  • smoothness assumption / Smoothness assumption
  • cluster assumption / Cluster assumption
  • manifold assumption / Manifold assumption
• Asynchronous Update / Strategies for distributed execution
• atrous convolution / Atrous convolution, Sampling the layers by convolution
• augmentation techniques
  • about / Augmentation techniques 
  • flipping / Augmentation techniques 
  • random cropping / Augmentation techniques 
  • shearing / Augmentation techniques 
  • zooming / Augmentation techniques 
  • rotation / Augmentation techniques 
  • whitening / Augmentation techniques 
  • normalization / Augmentation techniques 
  • channel shifting / Augmentation techniques 
• autoencoder
  • denoising, in TensorFlow / Denoising autoencoder in TensorFlow
  • denoising, in Keras / Denoising autoencoder in Keras
• autoencoders
  • used, for image denoising / Denoising using autoencoders
• autoencoder types
  • simple autoencoder / Autoencoder types
  • sparse autoencoder / Autoencoder types
  • Denoising autoencoder (DAE) / Autoencoder types
  • Convolutional autoencoder (CAE) / Autoencoder types
  • Variational autoencoder (VAE) / Autoencoder types
• average pooling / Pooling layers

B

• backpropagation / Training neural networks, Backpropagation
• backpropagation through time (BPTT) / Backpropagation through time (BPTT)
• Ball Trees / Ball Trees
• batch normalization / Batch normalization
• Bayes' theorem / Conditional probabilities and Bayes' theorem
• Bayes accuracy / Underfitting
• Bayesian network
  • about / Bayesian networks
  • sampling from / Sampling from a Bayesian network
  • direct sampling / Direct sampling
  • Markov chains / A gentle introduction to Markov chains
  • Gibbs sampling / Gibbs sampling
  • Metropolis-Hastings sampling / Metropolis-Hastings sampling
• bidimensional discrete convolutions
  • about / Bidimensional discrete convolutions
  • padding / Strides and padding
  • strides / Strides and padding
• Bidirectional RNN (BRNN) / RNN variants
• binary classification / Label propagation based on Markov random walks
  • logistic regression / Logistic regression for binary classification
  • about / Binary classification
• bottleneck features
  • training / Training on bottleneck features
• bottleneck layer / Autoencoders of raw images
• brute-force algorithm / k-Nearest Neighbors

C

• candidate-generating distribution / Metropolis-Hastings sampling
• capacity, models
  • defining / Capacity of a model
  • Vapnik-Chervonenkis capacity /
• cartpole game
  • reference / OpenAI Gym 101
  • simple policies, applying / Applying simple policies to a cartpole game
• categorical cross-entropy / Categorical cross-entropy
• cells / RNN variants
• chain rule of probabilities / Conditional probabilities and Bayes' theorem
• Chapman-Kolmogorov / A gentle introduction to Markov chains
• CIFAR10
  • ConvNets, using with TensorFlow / ConvNets for CIFAR10 with TensorFlow
  • ConvNets, using with Keras / ConvNets for CIFAR10 with Keras
• CIFAR10 Data
  • LeNet / LeNet for CIFAR10 Data
• classification
  • logistic regression, using / Classification using logistic regression
• class rebalancing / Example of label propagation based on Markov random walks
• COCO object detection challenge / COCO object detection challenge
• Common Objects in Context (COCO) / COCO object detection challenge
• completeness score / Completeness score
• Computed Tomography (CT) / Diagnosing medical images
• Computer Unified Device Architecture (CUDA) / Computer Unified Device Architecture - CUDA
• conditional independence / Conditional probabilities and Bayes' theorem
• conditional probability / Conditional probabilities and Bayes' theorem
• conditional random field (CRF) / DeepLab
• conditions
  • asserting, on with tf.Assert() function / Asserting on conditions with tf.Assert()
• connected layer
  • training, as convolution layer / Training a fully connected layer as a convolution layer
• consistent estimator / Bias of an estimator
• constant error carousel (CEC) / LSTM
• Constraint Optimization by Linear Approximation (COBYLA) / Example of S3VM
• content-based image retrieval (CBIR)
  • about / Content-based image retrieval
  • Locality sensitive hashing (LSH) / Content-based image retrieval
  • multi-index hashing / Content-based image retrieval
  • geometric verification / Content-based image retrieval
  • query expansion / Content-based image retrieval
  • relevance feedback / Content-based image retrieval
  • retrieval pipeline, building / Building the retrieval pipeline
  • efficient retrieval / Efficient retrieval
  • autoencoders, used for image denoising / Denoising using autoencoders
• Contrastive Pessimistic Likelihood Estimation (CPLE) algorithm
  • about / Contrastive pessimistic likelihood estimation
  • example / Example of contrastive pessimistic likelihood estimation
• ConvNets
  • training, with TensorFlow / ConvNets for CIFAR10 with TensorFlow
  • training, with Keras / ConvNets for CIFAR10 with Keras
• Convolution
  • about / Understanding convolution
  • reference / Understanding convolution
• convolutional LSTM / LSTM
• convolutional neural networks (CNN)
  • about / Convolutional neural network
  • kernel / Kernel
  • max pooling / Max pooling
• convolution implementation
  • of sliding window / Convolution implementation of sliding window
• convolutions
  • about / Convolutions
  • bidimensional discrete convolutions / Bidimensional discrete convolutions
  • separable convolution / Separable convolution
  • transpose convolution / Transpose convolution
• cost function
  • about / Loss and cost functions
  • starting point / Loss and cost functions
  • local minima / Loss and cost functions
  • ridges/local maxima / Loss and cost functions
  • plateaus / Loss and cost functions
  • global minimum / Loss and cost functions
  • examples / Examples of cost functions
  • mean squared error / Mean squared error
  • Huber cost function / Huber cost function
  • Hinge cost function / Hinge cost function
  • categorical cross-entropy / Categorical cross-entropy
  • regularization / Regularization
• covariance rule
  • about / Hebb's rule
  • analysis / Analysis of the covariance rule
  • application, example / Example of covariance rule application
  • example / Example of covariance rule application
• Cramér-Rao bound / The Cramér-Rao bound
• cross-entropy / Cross-entropy
• cross-validation / Cross-validation
• CUDA Deep Neural Network (CUDNN)
  • about / CUDA Deep Neural Network - CUDNN
  • URL, for downloading / CUDA Deep Neural Network - CUDNN
• CUDA library
  • URL, for downloading / Computer Unified Device Architecture - CUDA

D

• data / Models and data
  • preparing / Preparing the data
• data generating process / Models and data
• data parallel strategy
  • In-Graph Replication / Strategies for distributed execution
  • Between-Graph Replication / Strategies for distributed execution
• datasets
  • augmenting / Augmenting the dataset
  • augmentation techniques / Augmentation techniques 
  • exploring / Exploring the datasets
  • PASCAL VOC challenge / PASCAL VOC challenge
  • COCO object detection challenge / COCO object detection challenge
  • evaluating, metrics used / Evaluating datasets using metrics
  • Intersection over Union (IoU) / Intersection over Union
  • mean average precision / The mean average precision
  / Datasets
• decoder / Autoencoders of raw images
• deconvolution / Sampling the layers by convolution
• Deep Bidirectional RNN (DBRNN) / RNN variants
• Deep Convolutional GAN
  • with TensorFlow / Deep Convolutional GAN with TensorFlow and Keras
  • with Keras / Deep Convolutional GAN with TensorFlow and Keras
• deep convolutional network, with data augmentation
  • example / Example of a deep convolutional network with Keras and data augmentation
• deep convolutional network, with Keras
  • example / Examples of deep convolutional networks with Keras, Example of a deep convolutional network with Keras and data augmentation
• deep convolutional networks
  • about / Deep convolutional networks
  • convolutions / Convolutions
  • pooling layers / Pooling layers
  • padding layers / Other useful layers
  • upsampling layers / Other useful layers
  • cropping layers / Other useful layers
  • flattening layers / Other useful layers
• DeepDream / The DeepDream
• DeepLab / DeepLab
• DeepLab v3 / DeepLab v3
• deep learning
  • about / Understanding deep learning
  • perceptron / Perceptron
  • activation functions / Activation functions
  • artificial neural network (ANN) / Artificial neural network (ANN)
  • artificial neural network (ANN), training / Training neural networks
  • TensorFlow playground, playing / Playing with TensorFlow playground
  • convolutional neural networks (CNN) / Convolutional neural network
  • recurrent neural networks (RNN) / Recurrent neural networks (RNN)
  • long short-term memory (LSTM) / Long short-term memory (LSTM)
  • for computer vision / Deep learning for computer vision
  • classification / Classification
  • detection or localization / Detection or localization and segmentation
  • segmentation / Detection or localization and segmentation
  • similarity learning / Similarity learning
  • image captioning / Image captioning
  • generative models / Generative models
  • video analysis / Video analysis
• deep learning models
  • about / The bigger deep learning models
  • AlexNet model / The AlexNet model
  • VGG-16 model / The VGG-16 model
  • Google Inception-V3 model / The Google Inception-V3 model
  • Microsoft ResNet-50 model / The Microsoft ResNet-50 model
  • SqueezeNet model / The SqueezeNet model
  • spatial transformer networks / Spatial transformer networks
  • DenseNet model / The DenseNet model
• DeepNet model / The DeepNet model
• Deep Neural Networks (DNN) / MultiLayer Perceptron
• Deep Q Network (DQN)
  • Q-Learning / Q-Learning with Q-Network  or Deep Q Network (DQN) 
• DeepRank / DeepRank
• degree matrix / Label propagation
• DenseNet model / The DenseNet model
• depth multiplier / Separable convolution
• depthwise separable convolution / Separable convolution
• development environment
  • setting up / Development environment setup
  • hardware / Hardware and Operating Systems - OS
  • operating systems / Hardware and Operating Systems - OS
  • software packages, installing / Installing software packages
• Development Operating Systems(OS) / Development environment setup
• Dijkstra algorithm / Isomap
• dilated convolution / Atrous convolution, Sampling the layers by convolution, Dilated convolutions
• direct sampling
  • about / Direct sampling
  • example / Example of direct sampling
• discrete Laplacian operator / Bidimensional discrete convolutions
• Docker containers
  • TF Serving / TF Serving in the Docker containers
  • installing / Installing Docker
  • model, serving / Serving the model in the Docker container
• dockerhub
  • Docker image, uploading / Uploading the Docker image to the dockerhub
• Docker image
  • building, for TensorFlow Serving (TFS) / Building a Docker image for TF serving
  • uploading, to dockerhub / Uploading the Docker image to the dockerhub
• dropout / Dropout
• Dunn's partitioning coefficient / Fuzzy C-means

E

• early stopping / Early stopping
• efficient retrieval
  • about / Efficient retrieval
  • approximate nearest neighbor, used for faster matching / Matching faster using approximate nearest neighbour
  • raw images, autoencoders / Autoencoders of raw images
• ElasticNet / ElasticNet
• EleasticNet regularization / ElasticNet regularization
• emissions / Hidden Markov Models (HMMs)
• empirical risk / Loss and cost functions
• encoder / Content-based image retrieval, Autoencoders of raw images
• entropy function
  • reference / TensorFlow-based MLP for MNIST classification
• estimator
  • bias, measuring / Bias of an estimator
  • underfitting / Underfitting
  • variance, measuring / Variance of an estimator
  • overfitting / Overfitting
  • Cramér-Rao bound / The Cramér-Rao bound
• Euclidean distance / Computing similarity between query image and target database
• evaluation metrics
  • about / Evaluation metrics
  • homogeneity score / Homogeneity score
  • completeness score / Completeness score
  • Adjusted Rand Index / Adjusted Rand Index
  • silhouette score / Silhouette score
• Expectation Maximization (EM) algorithm
  • about / Models and data, EM algorithm
  • parameter estimation, example / An example of parameter estimation
• expected risk / Loss and cost functions

F

• face clustering / Face clustering 
• Face Detection Data Set and Benchmark (FDDB) / Face detection
• face landmarks
  • Multi-Task Facial Landmark (MTFL) dataset / The Multi-Task Facial Landmark (MTFL) dataset
  • Kaggle keypoint dataset / The Kaggle keypoint dataset
  • Multi-Attribute Facial Landmark (MAFL) dataset / The Multi-Attribute Facial Landmark (MAFL) dataset
  • facial key points, learning / Learning the facial key points
• FaceNet
  • about / FaceNet
  • face verification / FaceNet
  • face recognition / FaceNet
  • face clustering / FaceNet
  • triplet loss / Triplet loss
• face pose / Face landmarks and attributes
• face recognition
  • about / Face recognition
  • labeled faces, in LFW dataset / The labeled faces in the wild (LFW) dataset
  • YouTube faces dataset / The YouTube faces dataset
  • CelebFaces Attributes dataset (CelebA) / The CelebFaces Attributes dataset (CelebA) 
  • CASIA web face database / CASIA web face database
  • VGGFace2 dataset / The VGGFace2 dataset
  • similarity between faces, computing / Computing the similarity between faces
  • optimum threshold, finding / Finding the optimum threshold
• facial keypoints
  • about / Face landmarks and attributes
  • learning / Learning the facial key points
• facial keypoints detection
  • URL, for downloading / The Kaggle keypoint dataset
• factor analysis (FA) / Factor analysis
• factor analysis (FA), with Scikit-Learn
  • example / An example of factor analysis with Scikit-Learn
• FastICA with Scikit-Learn
  • example / An example of FastICA with Scikit-Learn
• Fast R-CNN / Fast R-CNN, Faster R-CNN
• FCN
  • modeling, for segmentation / Modeling FCN for segmentation
• feature map / Convolutions
• feed forward neural networks (FFNN) / MultiLayer Perceptron
• fiducial point detection / Applying regression to other problems
• fiducial points / Face landmarks and attributes
• fine-tuning / Fine-tuning several layers in deep learning
• Fisher information / The Cramér-Rao bound
• forward-backward algorithm
  • about / Forward-backward algorithm
  • forward phase / Forward phase
  • backward phase / Backward phase
  • HMM parameter estimation / HMM parameter estimation
• fractionally strided convolution / Sampling the layers by convolution
• Fully Convolutional Network (FCN) / The Fully Convolutional Network
• fuzzy C-means / Fuzzy C-means
• fuzzy C-means, with Scikit-Fuzzy
  • example / Example of fuzzy C-means with Scikit-Fuzzy
• fuzzy logic / Fuzzy C-means

G

• Gated recurrent unit (GRU) / GRU
• Gated Recurrent Unit (GRU)
  • about / RNN variants, GRU network
• Gaussian mixture / Gaussian mixture
• Gaussian mixture, with Scikit-Learn
  • example / An example of Gaussian Mixtures using Scikit-Learn
• Generalized Hebbian Rule (GHA) / Sanger's network
• General Purpose - Graphics Processing Unit (GP-GPU)
  • about / Hardware and Operating Systems - OS, General Purpose - Graphics Processing Unit (GP-GPU)
  • Computer Unified Device Architecture (CUDA) / Computer Unified Device Architecture - CUDA
  • CUDA Deep Neural Network (CUDNN) / CUDA Deep Neural Network - CUDNN
• Generative Adversarial Networks (GAN)
  • about / Generative Adversarial Networks 101
  • reference / Generative Adversarial Networks 101
  • TensorFlow, using / Simple GAN with TensorFlow
  • Keras, using / Simple GAN with Keras
• Generative Gaussian mixtures
  • about / Generative Gaussian mixtures
  • example / Example of a generative Gaussian mixture
  • weighted log-likelihood / Weighted log-likelihood
• Gibbs sampling / Gibbs sampling
• Google Cloud Platform (GCP) / Development environment setup
• Google Inception-V3 model / The Google Inception-V3 model
• gradient descent
  • reference / Defining the optimizer function
  / Gradient descent
• Gram-Schmidt / Sanger's network
• Graphics Processing Unit (GPU) / Preparing the data
• graph variables
  • saving, with saver class / Saving and restoring all graph variables with the saver class
  • restoring, with saver class / Saving and restoring all graph variables with the saver class
• ground truth / Evaluating datasets using metrics, Intersection over Union

H

• hard negative mining / Triplet loss
• hardware / Hardware and Operating Systems - OS
• Hebb's rule / Hebb's rule
• Hidden Markov Models (HMMs)
  • about / Hidden Markov Models (HMMs), Addendum to HMMs
  • forward-backward algorithm / Forward-backward algorithm
  • Viterbi algorithm / Viterbi algorithm
• Hinge cost function / Hinge cost function
• hmmlearn
  • reference link / Example of HMM training with hmmlearn
  • most likely hidden state sequence, finding / Finding the most likely hidden state sequence with hmmlearn
• HMM parameter estimation / HMM parameter estimation
• HMM training
  • hmmlearn / Example of HMM training with hmmlearn
• homogeneity score / Homogeneity score
• Huber cost function / Huber cost function
• human face analysis
  • about / Human face analysis
  • face detection / Face detection
  • face landmarks / Face landmarks and attributes
  • attributes / Face landmarks and attributes
  • face recognition / Face recognition
  • face clustering / Face clustering 
• hyperbolic tangent / The perceptron
• hyperbolic tangent function / The hyperbolic tangent function

I

• image classification
  • multilayer perceptron / MLP for image classification
• ImageNet dataset / ImageNet dataset
• Inception-V3 / The Google Inception-V3 model
• independent and identically distributed (i.i.d.) / Models and data
• independent component analysis / Independent component analysis
• inductive learning / Inductive learning
• inference / Model inference
• instance-based learning / k-Nearest Neighbors
• instance segmentation / Predicting pixels
• International Society for Photogrammetry and Remote Sensing (ISPRS) / Segmenting satellite images
• Intersection over Union (IoU) / Evaluating datasets using metrics, Intersection over Union
• Isomap algorithm
  • about / Isomap
  • example / Example of Isomap

K

• K-Fold cross-validation
  • about / Cross-validation
  • Stratified K-Fold / Cross-validation
  • Leave-one-out (LOO) / Cross-validation
  • Leave-P-out (LPO) / Cross-validation
• K-means / K-means
• K-means++ / K-means++
• K-means, with Scikit-Learn
  • example / Example of K-means with Scikit-Learn
• k-Nearest Neighbors (KNN)
  • about / k-Nearest Neighbors
  • KD Trees / KD Trees
  • Ball Trees / Ball Trees
• kaggle
  • about / Preparing the data
  • URL / Preparing the data
• Kaggle keypoint dataset / The Kaggle keypoint dataset
• KD Trees / KD Trees
• Keras
  • for RNN / Keras for RNN
  • RNN, for MNIST data / RNN in Keras for MNIST data
  • stacked autoencoder / Stacked autoencoder in Keras
  • autoencoder, denoising / Denoising autoencoder in Keras
  • variational autoencoder / Variational autoencoder in Keras
  • using, in Deep Convolutional GAN / Deep Convolutional GAN with TensorFlow and Keras
• Keras library / The Keras library
• Keras model
  • restoring / Saving and restoring Keras models
  • saving / Saving and restoring Keras models
• kernel / Kernel
• KNN, with Scikit-Learn
  • example / Example of KNN with Scikit-Learn
• Kohonen / Self-organizing maps
• Kubernetes
  • TensorFlow Serving (TFS) / TensorFlow Serving on Kubernetes
  • reference / TensorFlow Serving on Kubernetes
  • installing / Installing Kubernetes
  • deployment / Deploying in Kubernetes

L

• L1 regularization / L1 and L2 regularization
• label propagation
  • about / Label propagation
  • example / Example of label propagation
• label propagation, based on Markov random walks
  • about / Label propagation based on Markov random walks
  • example / Example of label propagation based on Markov random walks
• label spreading
  • about / Label spreading
  • example / Example of label spreading
• Laplacian Spectral Embedding
  • about / Laplacian Spectral Embedding
  • example / Example of Laplacian Spectral Embedding
• large kernel matters / Large kernel matters
• Lasso regularization / Lasso, Lasso regularization
• Latent Dirichlet Allocation (LDA) / MLE and MAP learning
• layers
  • fine-tuning, in deep learning / Fine-tuning several layers in deep learning
• Leave-one-out (LOO) / Cross-validation
• Leave-P-out (LPO) / Cross-validation
• LeNet
  • about / CNN architecture pattern - LeNet
  • reference / CNN architecture pattern - LeNet
  • for MNIST Data / LeNet for MNIST data
  • for CIFAR10 Data / LeNet for CIFAR10 Data
• LeNet CNN
  • building, for MNIST data with TensorFlow / LeNet CNN for MNIST with TensorFlow
  • building, for MNIST with Keras / LeNet CNN for MNIST with Keras
• likelihood / Conditional probabilities and Bayes' theorem
• linear regression
  • about / Simple linear regression
  • data preparation / Data preparation
  • model, building / Building a simple regression model
• Lloyd's algorithm / K-means
• Locally Linear Embedding (LLE)
  • about / Locally linear embedding
  • example / Example of locally linear embedding
• Local Response Normalization (LRN) / The AlexNet model
• logistic regression
  • used, for classification / Classification using logistic regression
  • for binary classification / Logistic regression for binary classification
  • multiclass classification / Logistic regression for multiclass classification
• long-short-term memory (LSTM) / LSTM
• long-term depression (LTD) / Hebb's rule
• long-term potentiation (LTP) / Hebb's rule
• long short-term memory (LSTM) / Long short-term memory (LSTM)
• Long Short-Term Memory (LSTM) network / RNN variants, LSTM network
• loss function
  • about / Loss and cost functions
  • defining / Loss and cost functions
• LSTM network, with Keras
  • example / Example of an LSTM network with Keras

M

• Magnetic Resonance Imaging (MRI) / Diagnosing medical images
• manifold learning
  • about / Manifold learning
  • Isomap algorithm / Isomap
  • Locally Linear Embedding (LLE) / Locally linear embedding
• Markov chains / A gentle introduction to Markov chains
• Mask RCNN / Segmenting instances
• Maximum A Posteriori (MAP) learning / MLE and MAP learning
• Maximum Likelihood Estimation (MLE) learning / MLE and MAP learning, Hebb's rule
• max pooling / Pooling layers, Max pooling
• mean average precision / The mean average precision
• Mean Precision Average (mAP) / Evaluating datasets using metrics
• mean squared error / Mean squared error
• mean squared error (mse) / Defining the loss function
• memory replay / Q-Learning with Q-Network  or Deep Q Network (DQN) 
• metric multidimensional scaling / Isomap
• Metropolis-Hastings sampling
  • about / Metropolis-Hastings sampling
  • example / Example of Metropolis-Hastings sampling
• Microsoft ResNet-50 model / The Microsoft ResNet-50 model
• MLLE
  • reference link / Locally linear embedding
• MNIST classification
  • MLP, using / TensorFlow-based MLP for MNIST classification
• MNIST data
  • RNN in Keras / RNN in Keras for MNIST data
  • LeNet / LeNet for MNIST data
  • LeNet CNN, building with TensorFlow / LeNet CNN for MNIST with TensorFlow
  • LeNet CNN, building with Keras / LeNet CNN for MNIST with Keras
• model
  • training / Training a model for cats versus dogs
  • data, preparing / Preparing the data
  • simple CNN, benchmarking / Benchmarking with simple CNN
  • dataset, augmenting / Augmenting the dataset
  • transfer learning / Transfer learning or fine-tuning of a model
  • fine-tuning / Transfer learning or fine-tuning of a model
  • bottleneck features, training / Training on bottleneck features
  • layers, fine-tuning, in deep learning / Fine-tuning several layers in deep learning
  • layers, fine-tuning in deep learning / Fine-tuning several layers in deep learning
• model inference
  • about / Model inference
  • exporting / Exporting a model
  • trained model, serving / Serving the trained model 
• models
  • about / Models and data, TensorFlow Serving
  • zero-centering / Zero-centering and whitening
  • whitening / Zero-centering and whitening
  • training set / Training and validation sets
  • validation set / Training and validation sets
  • cross-validation / Cross-validation
  • saving / Saving and Restoring models in TensorFlow
  • restoring / Saving and Restoring models in TensorFlow
• models, features
  • about / Features of a machine learning model
  • capacity, defining / Capacity of a model
  • estimator bias, measuring / Bias of an estimator
  • estimator variance, measuring / Variance of an estimator
• Modified LLE / Locally linear embedding
• MSE
  • reference / Defining the loss function
• Multi-Attribute Facial Landmark (MAFL) dataset / The Multi-Attribute Facial Landmark (MAFL) dataset
  • URL, for downloading / The Multi-Attribute Facial Landmark (MAFL) dataset
• Multi-Attribute Labelled Faces (MALF) / Face detection
• multi-regression / Multi-regression
• Multi-Task Facial Landmark (MTFL) dataset / The Multi-Task Facial Landmark (MTFL) dataset
• multiclass classification
  • logistic regression / Logistic regression for multiclass classification
  • about / Multiclass classification
• multidimensional regression / Multi-regression
• Multilayer Perceptron (MLP)
  • about / MultiLayer Perceptron
  • used, for image classification / MLP for image classification
  • building, with TensorFlow-based code / TensorFlow-based MLP for MNIST classification, Summary of MLP with TensorFlow, Keras, and TFLearn
  • Keras-based code / Keras-based MLP for MNIST classification
  • building, with TFLearn-based code / TFLearn-based MLP for MNIST classification, Summary of MLP with TensorFlow, Keras, and TFLearn
  • building, with Keras-based code / Summary of MLP with TensorFlow, Keras, and TFLearn
  • used, for time series regression / MLP for time series regression

N

• neural nets / Activation functions
• non-parametric models / Models and data

O

• object detection API
  • about / Object detection API
  • installing / Installation and setup
  • setting up / Installation and setup
  • pre-trained models / Pre-trained models
  • re-training object detection models / Re-training object detection models
  • pedestrian detection, training for self-driving car / Training a pedestrian detection for a self-driving car
• object localization / Detecting objects in an image
• objects
  • detecting, in an image / Detecting objects in an image
  • localizing, sliding windows used / Localizing objects using sliding windows
  • scale-space concept / The scale-space concept
  • connected layer, training as convolution layer / Training a fully connected layer as a convolution layer
  • convolution implementation, of sliding window / Convolution implementation of sliding window
  • detecting / Detecting objects
  • Regions of the convolutional neural network (R-CNN) / Regions of the convolutional neural network (R-CNN)
  • Fast R-CNN / Fast R-CNN, Faster R-CNN
  • Single shot multi-box detector / Single shot multi-box detector
• Occam's razor principle / The Cramér-Rao bound
• Oja's rule / Weight vector stabilization and Oja's rule
• one-hot encoding / One-hot encoding
• one-shot learning / Siamese networks
• OpenAI Gym
  • about / OpenAI Gym 101
  • reference / OpenAI Gym 101
• Open Computer Vision (OpenCV)
  • about / Open Computer Vision - OpenCV
  • URL / Open Computer Vision - OpenCV
• operating systems
  • about / Hardware and Operating Systems - OS
  • General Purpose - Graphics Processing Unit (GP-GPU) / General Purpose - Graphics Processing Unit (GP-GPU)
• optimization / Training neural networks
• Ordinary Least Squares (OLS) / Ridge
• overfitting / Overfitting, Regularized regression

P

• pandas
  • reference link / Example of an LSTM network with Keras
• parametric models / Models and data
• PASCAL VOC challenge
  • about / PASCAL VOC challenge
  • URL, for downloading / PASCAL VOC challenge
• PCA with Scikit-Learn
  • example / An example of PCA with Scikit-Learn
  • about / An example of PCA with Scikit-Learn
• pedestrain detection
  • training, for self-driving car / Training a pedestrian detection for a self-driving car
• peephole LSTM / LSTM
• perceptron / The perceptron
  • about / Perceptron
• pip3 / Python
• pixels
  • predicting / Predicting pixels
  • medical images, diagnosing / Diagnosing medical images
  • satellite imagery / Understanding the earth from satellite imagery
  • robots, enabling / Enabling robots to see
• point of inflection / Loss and cost functions
• policy search / V function (learning to optimize when the model is available)
• pooling
  • about / Understanding pooling
  • reference / Understanding pooling
  • layers, reference / Understanding pooling
• pooling layers / Pooling layers
• pose detection / Applying regression to other problems
• principal component analysis (PCA) / Understanding visual features
• Principal Component Analysis (PCA) / Isomap, Principal Component Analysis, Analysis of the covariance rule
• prior probability / Conditional probabilities and Bayes' theorem
• Protocol Buffers (protobuf) / Installation and setup
• PSPnet / PSPnet
• PyMC3
  • reference link / Sampling example using PyMC3
• Python / Python

Q

• Q-Learning
  • implementing / Implementing Q-Learning
  • discretizing / Initializing and discretizing for Q-Learning
  • initializing / Initializing and discretizing for Q-Learning
  • using, with Q-Table / Q-Learning with Q-Table
  • using, with Deep Q Network (DQN) / Q-Learning with Q-Network  or Deep Q Network (DQN) 
  • using, with Q-Network / Q-Learning with Q-Network  or Deep Q Network (DQN) 
• Q-Network
  • Q-Learning / Q-Learning with Q-Network  or Deep Q Network (DQN) 

R

• r-squared (rs) function / Defining the loss function
• Rayleigh-Ritz method / Locally linear embedding
• re-training object detection models
  • about / Re-training object detection models
  • data preparation, for Pet dataset / Data preparation for the Pet dataset
  • object detection training pipeline / Object detection training pipeline
  • model, training / Training the model
  • loss and accuracy monitoring, TensorBoard used / Monitoring loss and accuracy using TensorBoard
• real-world applications
  • developing / Developing real-world applications
  • model, selecting / Choosing the right model
  • underfitting, tackling / Tackling the underfitting and overfitting scenarios
  • scenarios, overfitting / Tackling the underfitting and overfitting scenarios
  • gender detection, from face / Gender and age detection from face
  • age detection, from face / Gender and age detection from face
  • apparel models, fine-tuning / Fine-tuning apparel models 
  • brand safety / Brand safety
• Rectified Linear Unit / The perceptron
• Rectified Linear Unit (ReLU) / The hyperbolic tangent function, The Rectified Linear Unit (ReLU)
• recurrent networks
  • about / Recurrent networks
  • backpropagation through time (BPTT) / Backpropagation through time (BPTT)
  • long-short-term memory (LSTM) / LSTM
  • Gated recurrent unit (GRU) / GRU
• recurrent neural networks (RNN)
  • about / Recurrent neural networks (RNN)
• Recurrent Neural Networks (RNNs)
  • variants / RNN variants
  • TensorFlow, using / TensorFlow for RNN
  • reference / TensorFlow RNN Cell Wrapper Classes, Application areas of RNNs
  • Keras / Keras for RNN
  • application areas / Application areas of RNNs
• Recurrent Neural Networks (RNNs) variants
  • about / RNN variants
  • bidirectional RNN (BRNN) / RNN variants
  • deep bidirectional RNN (DBRNN) / RNN variants
  • Long Short-Term Memory (LSTM) / RNN variants
  • Gated Recurrent Unit (GRU) / RNN variants
  • seq2seq models / RNN variants
• RefiNet / RefiNet
• Region of Interest pooling / Fast R-CNN
• Regions of the convolutional neural network (R-CNN) / Regions of the convolutional neural network (R-CNN)
• regression
  • about / Thinking about localization as a regression problem
  • applying / Applying regression to other problems
  • combining, with sliding window / Combining regression with the sliding window
• regression model
  • inputs, defining / Defining the inputs, parameters, and other variables
  • variables / Defining the inputs, parameters, and other variables
  • parameters / Defining the inputs, parameters, and other variables
  • defining / Defining the model
  • loss function, defining / Defining the loss function
  • training / Training the model
• regularization
  • about / Overfitting, Regularization
  • Ridge regularization / Ridge
  • Lasso regularization / Lasso
  • ElasticNet / ElasticNet
  • early stopping / Early stopping
• regularization models
  • Lasso regression / Regularized regression
  • Ridge regression / Regularized regression
  • ElasticNet regression / Regularized regression
  • reference / Regularized regression
• regularized regression / Regularized regression
• reinforcement learning
  • about / Reinforcement learning 101
  • Q function / Q function (learning to optimize when the model is not available)
  • exploration and exploitation / Exploration and exploitation in the RL algorithms
  • V function / V function (learning to optimize when the model is available)
  • techniques / Reinforcement learning techniques
  • Naive Neural Network policy / Naive Neural Network policy for Reinforcement Learning
• Remote Procedure Call (RPC) / Serving the trained model 
• representational capacity / Capacity of a model
• reset gate / GRU network
• residual / Defining the loss function
• ResNet / The Microsoft ResNet-50 model
• Retinopathy / Diagnosing medical images
• retrieval pipeline
  • about / Building the retrieval pipeline
  • building / Building the retrieval pipeline
  • bottleneck features, extracting for an image / Extracting bottleneck features for an image
  • similarity, computing between query image and target database / Computing similarity between query image and target database
• Ridge regularization / Ridge, Ridge regularization
• Rubner-Tavan's network
  • about / Rubner-Tavan's network
  • example / Example of Rubner-Tavan's network

S

• saddle points / Loss and cost functions
• same padding / Strides and padding
• Sanger's network
  • about / Sanger's network
  • example / Example of Sanger's network
• satellite images
  • segmenting / Segmenting satellite images
  • FCN, modeling for segmentation / Modeling FCN for segmentation
• saver class
  • used, for saving graph variables / Saving and restoring all graph variables with the saver class
  • used, for restoring selected variables / Saving and restoring selected  variables with the saver class
  • used, for saving selected variables / Saving and restoring selected  variables with the saver class
• scale-space concept / The scale-space concept
• scale space / Localizing objects using sliding windows
• Scikit-Fuzzy
  • reference link / Example of fuzzy C-means with Scikit-Fuzzy
• Scikit-Learn
  • label propagation / Label propagation in Scikit-Learn
• segmenting instances / Segmenting instances
• SegNet / The SegNet architecture
• SegNet architecture
  • about / The SegNet architecture
  • layers, upsampling by pooling / Upsampling the layers by pooling
  • layers, sampling by convolution / Sampling the layers by convolution
  • connections, skipping for training / Skipping connections for better training
• selected variables
  • restoring, with saver class / Saving and restoring selected  variables with the saver class
  • saving, with saver class / Saving and restoring selected  variables with the saver class
• Selective search / Regions of the convolutional neural network (R-CNN)
• Self-Organizing Maps (SOMs)
  • about / Self-organizing maps
  • example / Example of SOM
• semantic segmentation
  • about / Predicting pixels
  • algorithms / Algorithms for semantic segmentation
  • Fully Convolutional Network (FCN) / The Fully Convolutional Network
  • SegNet architecture / The SegNet architecture
  • dilated convolution / Dilated convolutions
  • DeepLab / DeepLab
  • RefiNet / RefiNet
  • PSPnet / PSPnet
  • large kernel matters / Large kernel matters
  • DeepLab v3 / DeepLab v3
• semi-supervised model
  • scenario / Semi-supervised scenario
  • transductive learning / Transductive learning
  • inductive learning / Inductive learning
  • assumptions / Semi-supervised assumptions
• semi-supervised Support Vector Machines (S3VM)
  • about / Semi-supervised Support Vector Machines (S3VM)
  • example / Example of S3VM
• separable convolution / Separable convolution
• seq2seq models / RNN variants
• Sequential Least Squares Programming (SLSQP) / Example of S3VM
• servables / TensorFlow Serving
• server class
  • used, for restoring graph variables / Saving and restoring all graph variables with the saver class
• shattering /
• Shi-Malik spectral clustering algorithm / Spectral clustering
• Siamese network
  • about / Siamese networks
  • contrastive loss / Contrastive loss
• Sigmoid / The perceptron
• sigmoid / Sigmoid
• silhouette score / Silhouette score
• similarity learning
  • algorithms / Algorithms for similarity learning
  • Siamese network / Siamese networks
  • FaceNet / FaceNet
  • DeepNet model / The DeepNet model
  • DeepRank / DeepRank
  • visual recommendation systems / Visual recommendation systems
• simple CNN
  • benchmarking / Benchmarking with simple CNN
• simple Recurrent Neural Network (RNN)
  • about / Simple Recurrent Neural Network
• Single shot multi-box detector / Single shot multi-box detector
• singular value decomposition (SVD) / Zero-centering and whitening, Principal Component Analysis
• sliding window / Localizing objects using sliding windows
• softmax / Softmax
• softmax function / Models and data
• software packages
  • installing / Installing software packages
  • Python / Python
  • Open Computer Vision (OpenCV) / Open Computer Vision - OpenCV
  • TensorFlow library / The TensorFlow library
  • Keras library / The Keras library
• sparse coding / Lasso
• spatial invariance / Spatial transformer networks
• spatial transformer networks / Spatial transformer networks
• spectral clustering / Spectral clustering
• spectral clustering, with Scikit-Learn
  • example / Example of spectral clustering with Scikit-Learn
• SqueezeNet model / The SqueezeNet model
• stacked autoencoder
  • about / Autoencoder types
  • in TensorFlow / Stacked autoencoder in TensorFlow
  • using / Stacked autoencoder in TensorFlow
  • in Keras / Stacked autoencoder in Keras
• Standard K-Fold / Cross-validation
• stochastic gradient descent (SGD) / Mean squared error, Gradient descent, Stochastic gradient descent
• Stochastic Gradient Descent (SGD) / The AlexNet model
• strategies, distributed execution
  • model parallel / Strategies for distributed execution
  • data parallel / Strategies for distributed execution
• Stratified K-Fold / Cross-validation
• strided convolution / Sampling the layers by convolution
• Support Vector Machine (SVM) / Cross-validation, Semi-supervised Support Vector Machines (S3VM), Brand safety
• Synchronous Update / Strategies for distributed execution

T

• t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • about / t-SNE
  • example / Example of t-distributed stochastic neighbor embedding 
  / Understanding visual features
• TensorFlow
  • Recurrent Neural Networks (RNNs) / TensorFlow for RNN
  • stacked autoencoder / Stacked autoencoder in TensorFlow
  • autoencoder, denoising / Denoising autoencoder in TensorFlow
  • variational autoencoder / Variational autoencoder in TensorFlow
  • models, restoring / Saving and Restoring models in TensorFlow
  • models, saving / Saving and Restoring models in TensorFlow
  • using, in Deep Convolutional GAN / Deep Convolutional GAN with TensorFlow and Keras
• TensorFlow clusters
  • about / TensorFlow clusters
  • specification, defining / Defining cluster specification
  • server instances, creating / Create the server instances
  • graph, defining for asynchronous updates / Define and train the graph for asynchronous updates
  • graph, training for asynchronous updates / Define and train the graph for asynchronous updates
  • graph, defining for synchronous updates / Define and train the graph for synchronous updates
  • graph, training for synchronous updates / Define and train the graph for synchronous updates
• TensorFlow debugger (tfdbg)
  • used, for debugging / Debugging with the TensorFlow debugger (tfdbg)
  • reference / Debugging with the TensorFlow debugger (tfdbg)
• TensorFlow library
  • about / The TensorFlow library
  • installing / Installing TensorFlow
  • example, to print Hello / TensorFlow example to print Hello, TensorFlow
  • example, for adding two numbers / TensorFlow example for adding two numbers
  • TensorBoard / TensorBoard
  • TensorFlow serving tool / The TensorFlow Serving tool
• TensorFlow playground
  • playing / Playing with TensorFlow playground
  • reference link / Playing with TensorFlow playground
• TensorFlow RNN cell classes / TensorFlow RNN Cell Classes
  • BasicRNNCell / TensorFlow RNN Cell Classes
  • BasicLSTMCell / TensorFlow RNN Cell Classes
  • LSTMCell / TensorFlow RNN Cell Classes
  • GRUCell / TensorFlow RNN Cell Classes
  • MultiRNNCell / TensorFlow RNN Cell Classes
  • LSTMBlockCell / TensorFlow RNN Cell Classes
  • LSTMBlockFusedCell / TensorFlow RNN Cell Classes
  • GLSTMCell / TensorFlow RNN Cell Classes
  • GridLSTMCell / TensorFlow RNN Cell Classes
  • GRUBlockCell / TensorFlow RNN Cell Classes
  • NASCell / TensorFlow RNN Cell Classes
  • UGRNNCell / TensorFlow RNN Cell Classes
• TensorFlow RNN cell wrapper classes / TensorFlow RNN Cell Wrapper Classes
• TensorFlow RNN model construction classes / TensorFlow RNN Model Construction Classes
• TensorFlow Serving (TFS)
  • about / TensorFlow Serving
  • installing / Installing TF Serving
  • models, saving / Saving models for TF Serving
  • models, serving / Serving models with TF Serving
  • in Docker containers / TF Serving in the Docker containers
  • Docker image, building / Building a Docker image for TF serving
  • Kubernetes / TensorFlow Serving on Kubernetes
• TensorFlow serving tool
  • reference link / The TensorFlow Serving tool
• tensor values
  • fetching, with tf.Session.run() function / Fetching tensor values with tf.Session.run()
  • printing, with tf.Print() function / Printing tensor values with tf.Print()
• tf.Assert() function
  • used, for asserting on conditions / Asserting on conditions with tf.Assert()
• tf.Print() function
  • used, for printing tensor values / Printing tensor values with tf.Print()
• tf.Session.run() function
  • used, for fetching tensor values / Fetching tensor values with tf.Session.run()
• Tikhonov regularization / Ridge
• time series datasets
  • conversion, reference / MLP for time series regression
• trained model
  • serving / Serving the trained model 
• trained regression model
  • using / Using the trained model to predict
• training set / Training and validation sets
• transductive learning / Transductive learning
• Transductive Support Vector Machines (TSVM)
  • about / Transductive Support Vector Machines (TSVM)
  • example / Example of TSVM
• transfer learning / Transfer learning
• transition probability / A gentle introduction to Markov chains
• transpose convolution / Transpose convolution
• transposed convolution / Sampling the layers by convolution
• triplet loss / FaceNet, Triplet loss
• truncated backpropagation through time (TBPTT) / Backpropagation through time (BPTT)

U

• ultra-nerve segmentation / Ultra-nerve segmentation
• unbiased estimator / Bias of an estimator
• underfitting / Underfitting
• up-convolution / Sampling the layers by convolution
• update gate / GRU network

V

• validation set / Training and validation sets
• valid padding / Strides and padding
• Vapnik-Chervonenkis-capacity /
• Vapnik-Chervonenkis theory /
• variational autoencoder
  • in TensorFlow / Variational autoencoder in TensorFlow
  • about / Variational autoencoder in TensorFlow
  • in Keras / Variational autoencoder in Keras
• VC-capacity /
• VC-dimension /
• VGG-16 model / The VGG-16 model
• VGGFace2 dataset
  • reference link / The VGGFace2 dataset
• visual features
  • about / Understanding visual features
  • nearest neighbor / Understanding visual features
  • dimensionality reduction / Understanding visual features
  • maximal patches / Understanding visual features
  • occlusion / Understanding visual features
  • deep learning models, activation visualizing / Visualizing activation of deep learning models
  • DeepDream / The DeepDream
  • adversarial examples / Adversarial examples
• Visual Geometry Group (VGG) / The VGG-16 model, Fine-tuning several layers in deep learning
• visualization
  • embedding / Embedding visualization
  • guided backpropagation / Guided backpropagation
• Viterbi algorithm / Viterbi algorithm

W

• weighted log-likelihood / Weighted log-likelihood
• weight shrinkage / Ridge
• weight vector
  • stabilization / Weight vector stabilization and Oja's rule
• whitening
  • about / Zero-centering and whitening
  • advantages / Zero-centering and whitening
• wider face / Face detection
• winner-takes-all / Self-organizing maps

Y

• YOLO object detection algorithm / The YOLO object detection algorithm 
• You look only once (YOLO) / The YOLO object detection algorithm 

Z

• zero-centering / Zero-centering and whitening