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▼a 9781788474559
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▼a 1717558
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▼a (OCoLC)1027155886
▼z (OCoLC)1027194881
▼z (OCoLC)1027356415
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▼b 01201872 |
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▼a 361CBCC8-C94D-472D-AC6F-4B0C12C84CBC
▼b OverDrive, Inc.
▼n http://www.overdrive.com |
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▼b eng
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▼d OCLCF
▼d MERUC
▼d CHVBK
▼d OCLCO
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▼a QA276.45.R3
▼b .L589 2018 |
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▼a 006.31
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▼a 006.31 |
| 100 | 1 |
▼a Liu, Yuxi (Hayden). |
| 245 | 10 |
▼a R Deep Learning Projects
▼h [electronic resource]:
▼b Master the techniques to design and develop neural network models in R. /
▼c Yuxi Liu. |
| 260 | |
▼a Birmingham:
▼b Packt Publishing,
▼c 2018. |
| 300 | |
▼a 1 online resource (253 pages). |
| 336 | |
▼a text
▼b txt
▼2 rdacontent |
| 337 | |
▼a computer
▼b c
▼2 rdamedia |
| 338 | |
▼a online resource
▼b cr
▼2 rdacarrier |
| 500 | |
▼a Exploratory data analysis. |
| 505 | 0 |
▼a 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. |
| 505 | 8 |
▼a 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. |
| 505 | 8 |
▼a 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. |
| 505 | 8 |
▼a 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. |
| 505 | 8 |
▼a 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. |
| 520 | |
▼a 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 ... |
| 650 | 0 |
▼a R. |
| 650 | 0 |
▼a Artificial intelligence. |
| 650 | 0 |
▼a Neural networks. |
| 650 | 7 |
▼a Artificial intelligence.
▼2 fast
▼0 (OCoLC)fst00817247 |
| 650 | 7 |
▼a MATHEMATICS / Applied
▼2 bisacsh |
| 650 | 7 |
▼a MATHEMATICS / Probability & Statistics / General
▼2 bisacsh |
| 655 | 4 |
▼a Electronic books. |
| 700 | 1 |
▼a Maldonado, Pablo. |
| 776 | 08 |
▼i Print version:
▼a Liu, Yuxi (Hayden).
▼t R Deep Learning Projects : Master the techniques to design and develop neural network models in R.
▼d Birmingham : Packt Publishing, ©2018 |
| 856 | 41 |
▼u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1717558 |