Cargando…
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 |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2018.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
| LEADER | 00000cam a2200000Mi 4500 | ||
|---|---|---|---|
| 001 | EBSCO_on1027155886 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr |n|---||||| | ||
| 008 | 180303s2018 enk ob 001 0 eng d | ||
| 040 | |a EBLCP |b eng |e pn |c EBLCP |d MERUC |d CHVBK |d OCLCO |d IDB |d OCLCF |d OCLCQ |d YDX |d VT2 |d TEFOD |d OCLCQ |d N$T |d C6I |d UKAHL |d AUD |d OCLCQ |d UKMGB |d OCLCQ |d K6U |d OCLCO |d OCLCQ |d PSYSI |d OCLCQ |d OCLCO | ||
| 015 | |a GBB849967 |2 bnb | ||
| 016 | 7 | |a 018788291 |2 Uk | |
| 019 | |a 1027194881 |a 1027356415 |a 1027556192 |a 1027713799 |a 1106146827 | ||
| 020 | |a 9781788474559 |q (electronic bk.) | ||
| 020 | |a 1788474554 |q (electronic bk.) | ||
| 020 | |a 1788478401 | ||
| 020 | |a 9781788478403 | ||
| 020 | |z 9781788478403 | ||
| 020 | |z 1788478401 | ||
| 024 | 3 | |a 9781788478403 | |
| 029 | 1 | |a AU@ |b 000062127688 | |
| 029 | 1 | |a AU@ |b 000066233001 | |
| 029 | 1 | |a CHNEW |b 000987253 | |
| 029 | 1 | |a CHVBK |b 509401708 | |
| 029 | 1 | |a UKMGB |b 018788291 | |
| 029 | 1 | |a AU@ |b 000067022843 | |
| 029 | 1 | |a AU@ |b 000067091526 | |
| 029 | 1 | |a AU@ |b 000072861775 | |
| 035 | |a (OCoLC)1027155886 |z (OCoLC)1027194881 |z (OCoLC)1027356415 |z (OCoLC)1027556192 |z (OCoLC)1027713799 |z (OCoLC)1106146827 | ||
| 037 | |a B08604 |b 01201872 | ||
| 037 | |a 361CBCC8-C94D-472D-AC6F-4B0C12C84CBC |b OverDrive, Inc. |n http://www.overdrive.com | ||
| 050 | 4 | |a QA276.45.R3 |b .L589 2018 | |
| 072 | 7 | |a MAT |x 003000 |2 bisacsh | |
| 072 | 7 | |a MAT |x 029000 |2 bisacsh | |
| 082 | 0 | 4 | |a 519.502855133 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Liu, Yuxi (Hayden) | |
| 245 | 1 | 0 | |a R Deep Learning Projects : |b Master the techniques to design and develop neural network models in R. |
| 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 | ||
| 588 | 0 | |a Print version record. | |
| 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. | |
| 500 | |a Exploratory data analysis. | ||
| 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 ... | ||
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a R. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Neural networks. | |
| 650 | 2 | |a Artificial Intelligence | |
| 650 | 6 | |a Intelligence artificielle. | |
| 650 | 7 | |a artificial intelligence. |2 aat | |
| 650 | 7 | |a MATHEMATICS |x Applied. |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS |x Probability & Statistics |x General. |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 700 | 1 | |a Maldonado, Pablo. | |
| 776 | 0 | 8 | |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 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1717558 |z Texto completo |
| 891 | |a .o11927185 | ||
| 938 | |a Askews and Holts Library Services |b ASKH |n AH33942487 | ||
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL5309083 | ||
| 938 | |a EBSCOhost |b EBSC |n 1717558 | ||
| 938 | |a YBP Library Services |b YANK |n 15185820 | ||
| 994 | |a 92 |b IZTAP | ||