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