R Deep Learning Projects [electronic resource] : Master the techniques to design and develop neural network models in R

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자료유형	단행본
서명/저자사항	R Deep Learning Projects [electronic resource]: Master the techniques to design and develop neural network models in R. / Yuxi Liu.
개인저자	Liu, Yuxi (Hayden).  Maldonado, Pablo.
발행사항	Birmingham: Packt Publishing, 2018.
형태사항	1 online resource (253 pages).
기타형태 저록	Print version: Liu, Yuxi (Hayden). R Deep Learning Projects : Master the techniques to design and develop neural network models in R. Birmingham : Packt Publishing, ©2018
ISBN	9781788474559 1788474554 1788478401 9781788478403
기타표준부호	9781788478403
일반주기	Exploratory data analysis.
내용주기	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.
요약	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 ...
일반주제명	R. Artificial intelligence. Neural networks. Artificial intelligence. MATHEMATICS / Applied MATHEMATICS / Probability & Statistics / General
분류기호(DDC)	006.31
언어	영어
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