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BL
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| Record Number
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856542
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| Main Entry
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Liu, Yuxi (Hayden)
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| Title & Author
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R Deep Learning Projects : : Master the techniques to design and develop neural network models in R.
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| Publication Statement
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Birmingham :: Packt Publishing,, 2018.
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| Page. NO
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1 online resource (253 pages)
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| ISBN
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1788474554
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: 1788478401
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: 9781788474559
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: 9781788478403
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1788478401
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9781788478403
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| Notes
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Exploratory data analysis.
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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.
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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.
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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.
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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.
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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.
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| Abstract
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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, traffic light detection, fraud detection, text ...
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| Subject
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Artificial intelligence.
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Neural networks.
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| Subject
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R.
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Artificial intelligence.
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| Subject
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MATHEMATICS-- Applied.
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| Subject
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MATHEMATICS-- Probability Statistics-- General.
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| Dewey Classification
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519.502855133
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| LC Classification
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QA276.45.R3.L589 2018
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| Added Entry
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Maldonado, Pablo.
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