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R Deep Learning Projects : Master the techniques to design and develop neural network models in R.
R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traf...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Handwritten Digit Recognition Using Convolutional Neural Networks; What is deep learning and why do we need it?; What makes deep learning special?; What are the applications of deep learning?; Handwritten digit recognition using CNNs; Get started with exploring MNIST; First attempt â#x80;#x93; logistic regression; Going from logistic regression to single-layer neural networks; Adding more hidden layers to the networks; Extracting richer representation with CNNs; Summary.
- Chapter 2: Traffic Sign Recognition for Intelligent VehiclesHow is deep learning applied in self-driving cars?; How does deep learning become a state-of-the-art solution?; Traffic sign recognition using CNN; Getting started with exploring GTSRB; First solution â#x80;#x93; convolutional neural networks using MXNet; Trying something new â#x80;#x93; CNNs using Keras with TensorFlow; Reducing overfitting with dropout; Dealing with a small training set â#x80;#x93; data augmentation; Reviewing methods to prevent overfitting in CNNs; Summary; Chapter 3: Fraud Detection with Autoencoders; Getting ready.
- Installing Keras and TensorFlow for RInstalling H2O; Our first examples; A simple 2D example; Autoencoders and MNIST; Outlier detection in MNIST; Credit card fraud detection with autoencoders; Exploratory data analysis; The autoencoder approach â#x80;#x93; Keras; Fraud detection with H2O; Exercises; Variational Autoencoders; Image reconstruction using VAEs; Outlier detection in MNIST; Text fraud detection; From unstructured text data to a matrix; From text to matrix representation â#x80;#x94; the Enron dataset; Autoencoder on the matrix representation; Exercises; Summary.
- Chapter 4: Text Generation Using Recurrent Neural NetworksWhat is so exciting about recurrent neural networks?; But what is a recurrent neural network, really?; LSTM and GRU networks; LSTM; GRU; RNNs from scratch in R; Classes in R with R6; Perceptron as an R6 class; Logistic regression; Multi-layer perceptron; Implementing a RNN; Implementation as an R6 class; Implementation without R6; RNN without derivatives â#x80;#x94; the cross-entropy method; RNN using Keras; A simple benchmark implementation; Generating new text from old; Exercises; Summary; Chapter 5: Sentiment Analysis with Word Embeddings.
- Warm-up â#x80;#x93; data explorationWorking with tidy text; The more, the merrier â#x80;#x93; calculating n-grams instead of single words; Bag of words benchmark; Preparing the data; Implementing a benchmark â#x80;#x93; logistic regression ; Exercises; Word embeddings; word2vec; GloVe; Sentiment analysis from movie reviews; Data preprocessing; From words to vectors; Sentiment extraction; The importance of data cleansing; Vector embeddings and neural networks; Bi-directional LSTM networks; Other LSTM architectures; Exercises; Mining sentiment from Twitter; Connecting to the Twitter API; Building our model.