| 000 | camMi | |
| 001 | 2210080853244 | |
| 003 | OCoLC | |
| 005 | 20190103135300 | |
| 006 | m d | |
| 007 | cr |n|---||||| | |
| 008 | 180303s2018 enk o 000 0 eng d | |
| 019 | ▼a1027194881▼a1027356415▼a1027556192▼a1027713799 | |
| 020 | ▼a9781788474559▼q(electronic bk.) | |
| 020 | ▼a1788474554▼q(electronic bk.) | |
| 020 | ▼z9781788478403 | |
| 020 | ▼z1788478401 | |
| 020 | ▼a1788478401 | |
| 020 | ▼a9781788478403 | |
| 024 | ▼a9781788478403 | |
| 035 | ▼a1717558▼b(N▼T) | |
| 035 | ▼a(OCoLC)1027155886▼z(OCoLC)1027194881▼z(OCoLC)1027356415▼z(OCoLC)1027556192▼z(OCoLC)1027713799 | |
| 037 | ▼aB08604▼b01201872 | |
| 037 | ▼a361CBCC8-C94D-472D-AC6F-4B0C12C84CBC▼bOverDrive, Inc.▼nhttp://www.overdrive.com | |
| 040 | ▼aEBLCP▼beng▼epn▼cEBLCP▼dMERUC▼dCHVBK▼dOCLCO▼dIDB▼dOCLCF▼dOCLCQ▼dYDX▼dVT2▼dTEFOD▼dOCLCQ▼dN▼dC6I▼d221008 | |
| 050 | ▼aQA276.45.R3▼b.L589 2018 | |
| 072 | ▼aMAT▼x003000▼2bisacsh | |
| 072 | ▼aMAT▼x029000▼2bisacsh | |
| 082 | ▼a519.502855133▼223 | |
| 100 | ▼aLiu, Yuxi (Hayden) | |
| 245 | 00 | ▼aR Deep Learning Projects :▼bMaster the techniques to design and develop neural network models in R. |
| 260 | ▼aBirmingham :▼bPackt Publishing,▼c2018. | |
| 300 | ▼a1 online resource (253 pages) | |
| 336 | ▼atext▼btxt▼2rdacontent | |
| 337 | ▼acomputer▼bc▼2rdamedia | |
| 338 | ▼aonline resource▼bcr▼2rdacarrier | |
| 500 | 00 | ▼aExploratory data analysis. |
| 505 | ▼aCover; 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 a?#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 | ▼aChapter 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 solutionA? a?#x80;#x93; convolutional neural networks using MXNet; Trying something newA? a?#x80;#x93; CNNs using Keras with TensorFlow; Reducing overfitting with dropout; Dealing with a small training setA? a?#x80;#x93; data augmentation; Reviewing methods to prevent overfitting in CNNs; Summary; Chapter 3: Fraud Detection with Autoencoders; Getting ready. | |
| 505 | ▼aInstalling 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 a?#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 a?#x80;#x94; the Enron dataset; Autoencoder on the matrix representation; Exercises; Summary. | |
| 505 | ▼aChapter 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 a?#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 | ▼aWarm-up a?#x80;#x93; data explorationWorking with tidy text; The more, the merrier a?#x80;#x93; calculating n-grams instead of single words; Bag of words benchmark; Preparing the data; Implementing a benchmark a?#x80;#x93; logistic regressionA? ; 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 | ▼aR 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 ... | |
| 588 | ▼aPrint version record. | |
| 590 | ▼aAdded to collection customer.56279.3 - Master record variable field(s) change: 072 | |
| 650 | ▼aR. | |
| 650 | ▼aArtificial intelligence. | |
| 650 | ▼aNeural networks. | |
| 650 | ▼aArtificial intelligence.▼2fast▼0(OCoLC)fst00817247 | |
| 650 | ▼aMATHEMATICS / Applied▼2bisacsh | |
| 650 | ▼aMATHEMATICS / Probability & Statistics / General▼2bisacsh | |
| 655 | ▼aElectronic books. | |
| 700 | 1 | ▼aMaldonado, Pablo. |
| 776 | ▼iPrint version:▼aLiu, Yuxi (Hayden).▼tR Deep Learning Projects : Master the techniques to design and develop neural network models in R.▼dBirmingham : Packt Publishing, 짤2018 | |
| 856 | ▼3EBSCOhost▼uhttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1717558 | |
| 938 | ▼aEBL - Ebook Library▼bEBLB▼nEBL5309083 | |
| 938 | ▼aYBP Library Services▼bYANK▼n15185820 | |
| 938 | ▼aEBSCOhost▼bEBSC▼n1717558 | |
| 994 | ▼a92▼bN |
R Deep Learning Projects :Master the techniques to design and develop neural network models in R
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R Deep Learning Projects :Master the techniques to design and develop neural network models in R
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Birmingham : Packt Publishing 2018.
형태사항
1 online resource (253 pages)
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Exploratory data analysis. / 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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