Index

A

• activation layer / Extracting richer representation with CNNs
• Adagrad / But what is a recurrent neural network, really?
• Adaptive Boosting (AdaBoost) / How does deep learning become a state-of-the-art solution?
• artificial neural network (ANN) / What is deep learning and why do we need it?
• autoencoder
  • example / Our first examples
  • about / Autoencoders and MNIST
  • credit card, fraud detection / Credit card fraud detection with autoencoders
  • exploratory data analysis / Exploratory data analysis
  • Keras / The autoencoder approach – Keras
  • fraud detection, with H2O / Fraud detection with H2O

B

• backpropagation algorithm / Going from logistic regression to single-layer neural networks
• backpropagation through time / But what is a recurrent neural network, really?
• benchmark
  • about / Bag of words benchmark
  • data, preparing / Preparing the data
  • implementing / Implementing a benchmark – logistic regression 
• Bi-directional LSTM networks / Bi-directional LSTM networks

C

• classification and regression training (caret) / First attempt – logistic regression
• co-occurrence matrix / GloVe
• Computer Vision and Pattern Recognition (CVPR) / How does deep learning become a state-of-the-art solution?
• convolutional layer / Extracting richer representation with CNNs
• convolutional neural networks (CNNs)
  • used, for handwritten digit recognition / Handwritten digit recognition using CNNs
  • used, for German Traffic Sign Recognition Benchmark (GTSRB) / Traffic sign recognition using CNN
  • MXNet, used / First solution – convolutional neural networks using MXNet
  • Keras, used with TensorFlow in CNNs / Trying something new – CNNs using Keras with TensorFlow
  • methods, reviewing to prevent overfitting / Reviewing methods to prevent overfitting in CNNs
• credit card
  • fraud detection, with autoencoder / Credit card fraud detection with autoencoders
• cross-entropy method / RNN without derivatives — the cross-entropy method

D

• 2D example / A simple 2D example
• data
  • preparing / Preparing the data
• data augmentation / Dealing with a small training set – data augmentation
• data cleansing / The importance of data cleansing
• data exploration
  • about / Warm-up – data exploration
  • tidy text, working / Working with tidy text
  • n-grams, calculating instead of single words / The more, the merrier – calculating n-grams instead of single words
• data preprocessing / Data preprocessing
• dataset
  • URL, for downloading / Exploratory data analysis
• deep learning
  • need for / What is deep learning and why do we need it?
  • about / What makes deep learning special?
  • applications / What are the applications of deep learning?
  • applying, in self-driving cars / How is deep learning applied in self-driving cars?
  • state-of-the-art solution / How does deep learning become a state-of-the-art solution?
• downsampling layer / Extracting richer representation with CNNs
• dropout / Reducing overfitting with dropout

E

• exploratory data analysis / Exploratory data analysis, Exploratory data analysis

F

• feature map / Extracting richer representation with CNNs
• forward propagation / But what is a recurrent neural network, really?
• fraud detection
  • with H2O / Fraud detection with H2O

G

• Gated recurrent units (GRUs) / GRU
• gates / LSTM
• German Traffic Sign Recognition Benchmark (GTSRB)
  • convolutional neural networks (CNNs), used / Traffic sign recognition using CNN
  • exploring / Getting started with exploring GTSRB
  • convolutional neural networks (CNNs), using MXNet / First solution – convolutional neural networks using MXNet
  • convolutional neural networks (CNNs), using Keras with TensorFlow / Trying something new – CNNs using Keras with TensorFlow
  • overfitting, reducing with dropout / Reducing overfitting with dropout
• global vectors (GloVe) / GloVe
• Google Neural Machine Translation system (GNMT) / What are the applications of deep learning?
• GRU networks / LSTM and GRU networks, GRU

H

• H2O
  • installing / Installing H2O
• handwritten digit recognition
  • CNNs, used / Handwritten digit recognition using CNNs
• hidden layer
  • about / What is deep learning and why do we need it?, Multi-layer perceptron
  • adding, to networks / Adding more hidden layers to the networks
• histogram of oriented gradients (HOG) / What makes deep learning special?, How does deep learning become a state-of-the-art solution?

I

• International Conference on Machine Learning (ICML) / What are the applications of deep learning?
• Internet of Things (IoT) / What are the applications of deep learning?

K

• kappa metric / Sentiment extraction
• Keras
  • URL / Trying something new – CNNs using Keras with TensorFlow
  • used, with TensorFlow / Trying something new – CNNs using Keras with TensorFlow
  • used, for Recurrent neural networks (RNNs) / Trying something new – CNNs using Keras with TensorFlow, RNN using Keras
  • about / Getting ready, The autoencoder approach – Keras
  • installing, for R / Installing Keras and TensorFlow for R
• Kullback-Leibler divergence / Variational Autoencoders

L

• Latent Dirichlet Allocation (LDA) / What makes deep learning special?
• latent variables / Variational Autoencoders
• Light Detection and Ranging (LiDAR) / How is deep learning applied in self-driving cars?
• logistic regression
  • about / First attempt – logistic regression
  • to single-layer neural networks / Going from logistic regression to single-layer neural networks
• LSTM / LSTM
• LSTM architectures / Other LSTM architectures
• LSTM networks / LSTM and GRU networks, LSTM

M

• memory / A simple benchmark implementation
• MNIST
  • about / What are the applications of deep learning?, Autoencoders and MNIST
  • exploring / Get started with exploring MNIST
  • outlier detection / Outlier detection in MNIST, Outlier detection in MNIST
• Momentum / Adding more hidden layers to the networks
• MXNet
  • used, for convolutional neural networks (CNNs) / First solution – convolutional neural networks using MXNet

N

• National Institute of Standards and Technology (NIST) / What are the applications of deep learning?
• natural language processing (NLP) / What are the applications of deep learning?, Word embeddings
• neural networks / Vector embeddings and neural networks
• non-linear layer / Extracting richer representation with CNNs

O

• overfitting
  • reducing, with dropout / Reducing overfitting with dropout

P

• pooling layer / Extracting richer representation with CNNs
• portable pixmap (PPM) / Getting started with exploring GTSRB
• precision-recall curve (AUC) / Credit card fraud detection with autoencoders
• principal component analysis (PCA) / What makes deep learning special?

R

• R6 class
  • perceptron / Perceptron as an R6 class
  • logistic regression / Logistic regression
  • multi-layer perceptron / Multi-layer perceptron
• receiver-operator characteristic (ROC) / Credit card fraud detection with autoencoders
• receptive field / Extracting richer representation with CNNs
• rectified linear unit (ReLU) / Adding more hidden layers to the networks
• recurrent neural network
  • about / What is so exciting about recurrent neural networks?, But what is a recurrent neural network, really?
  • LSTM networks / LSTM and GRU networks
  • GRU networks / LSTM and GRU networks
• Recurrent neural networks (RNNs)
  • about / What makes deep learning special?
  • from scratch in R / RNNs from scratch in R
  • implementing / Implementing a RNN
  • implementing, using R6 class / Implementation as an R6 class
  • implementing, without R6 class / Implementation without R6
  • without derivatives / RNN without derivatives — the cross-entropy method
  • Keras, used / RNN using Keras
  • benchmark implementation / A simple benchmark implementation
  • new text, generating from old text / Generating new text from old
• regions of interest (ROI) / Getting started with exploring GTSRB
• richer representation
  • extracting, with CNNs / Extracting richer representation with CNNs
• R language
  • RNNs, from scratch / RNNs from scratch in R
  • classes, with R6 / Classes in R with R6

S

• Scale Invariant Feature Transform (SIFT) / What makes deep learning special?, How does deep learning become a state-of-the-art solution?
• sentiment analysis
  • about / Sentiment analysis from movie reviews
  • data preprocessing / Data preprocessing
  • words, to vectors / From words to vectors
  • sentiment extraction / Sentiment extraction
  • data cleansing / The importance of data cleansing
  • vector embeddings / Vector embeddings and neural networks
  • neural networks / Vector embeddings and neural networks
  • vector embedding / Vector embeddings and neural networks
  • Bi-directional LSTM networks / Bi-directional LSTM networks
  • LSTM architectures / Other LSTM architectures
  • mining, from Twitter / Mining sentiment from Twitter
  • Twitter API, connecting / Connecting to the Twitter API
  • model, building / Building our model
• Speeded Up Robust Features (SURF) / What makes deep learning special?, How does deep learning become a state-of-the-art solution?
• stochastic gradient descent (SGD) / Trying something new – CNNs using Keras with TensorFlow
• Support Vector Machine (SVM) / What are the applications of deep learning?, How does deep learning become a state-of-the-art solution?

T

• TensorFlow
  • used, in CNNs with Keras / Trying something new – CNNs using Keras with TensorFlow
  • URL / Trying something new – CNNs using Keras with TensorFlow
  • about / Getting ready
  • installing, for R / Installing Keras and TensorFlow for R
• text fraud detection
  • about / Text fraud detection
  • unstructured text data, to matrix / From unstructured text data to a matrix
  • text, to matrix representation / From text to matrix representation — the Enron dataset
  • autoencoder, on matrix representation / Autoencoder on the matrix representation
• tidy text
  • working / Working with tidy text
• trained model
  • using / Using a trained model
• Twitter API
  • connecting / Connecting to the Twitter API

U

• unigrams / Data preprocessing
• unstructured text data
  • to matrix representation / From unstructured text data to a matrix

V

• Variational Autoencoders (VAE)
  • about / Variational Autoencoders
  • used, for image reconstruction / Image reconstruction using VAEs
  • outlier detection, in MNIST / Outlier detection in MNIST
• vector embedding / Vector embeddings and neural networks
• vector embeddings / Vector embeddings and neural networks
• vectors
  • to words / From words to vectors

W

• word2vec / word2vec
• word embeddings
  • about / Word embeddings
  • word2vec / word2vec
  • GloVe / GloVe
• words
  • to vectors / From words to vectors