Deep Learning with R for Beginners :: Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet.
This Learning Path is your step-by-step guide to building deep learning models using R's wide range of deep learning libraries and frameworks. Through multiple real-world projects and expert guidance and tips, you'll gain the exact knowledge you need to get started with developing deep mod...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | , , |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | This Learning Path is your step-by-step guide to building deep learning models using R's wide range of deep learning libraries and frameworks. Through multiple real-world projects and expert guidance and tips, you'll gain the exact knowledge you need to get started with developing deep models using R. |
| Beschreibung: | Document classification |
| Beschreibung: | 1 online resource (605 pages) |
| Bibliographie: | Includes bibliographical references and index. |
| ISBN: | 1838647228 9781838647223 |
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| 245 | 1 | 0 | |a Deep Learning with R for Beginners : |b Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2019. | ||
| 300 | |a 1 online resource (605 pages) | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |a Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; What is deep learning?; A conceptual overview of neural networks; Neural networks as an extension of linear regression; Neural networks as a network of memory cells; Deep neural networks; Some common myths about deep learning; Setting up your R environment; Deep learning frameworks for R; MXNet; Keras; Do I need a GPU (and what is it, anyway)?; Setting up reproducible results; Summary; Chapter 2: Training a Prediction Model; Neural networks in R | |
| 505 | 8 | |a Building neural network models; Generating predictions from a neural network; The problem of overfitting data -- the consequences explained; Use case -- building and applying a neural network; Summary; Chapter 3: Deep Learning Fundamentals; Building neural networks from scratch in R; Neural network web application; Neural network code; Back to deep learning; The symbol, X, y, and ctx parameters; The num.round and begin.round parameters; The optimizer parameter; The initializer parameter; The eval.metric and eval.data parameters; The epoch.end.callback parameter; The array.batch.size parameter | |
| 505 | 8 | |a Using regularization to overcome overfitting; L1 penalty; L1 penalty in action; L2 penalty; L2 penalty in action; Weight decay (L2 penalty in neural networks); Ensembles and model-averaging; Use case -- improving out-of-sample model performance using dropout; Summary; Chapter 4: Training Deep Prediction Models; Getting started with deep feedforward neural networks; Activation functions; Introduction to the MXNet deep learning library; Deep learning layers; Building a deep learning model; Use case -- using MXNet for classification and regression; Data download and exploration | |
| 505 | 8 | |a Preparing the data for our modelsThe binary classification model; The regression model; Improving the binary classification model; The unreasonable effectiveness of data; Summary; Chapter 5: Image Classification Using Convolutional Neural Networks; CNNs; Convolutional layers; Pooling layers; Dropout; Flatten layers, dense layers, and softmax; Image classification using the MXNet library; Base model (no convolutional layers); LeNet; Classification using the fashion MNIST dataset; References/further reading; Summary; Chapter 6: Tuning and Optimizing Models | |
| 505 | 8 | |a Evaluation metrics and evaluating performance; Types of evaluation metric; Evaluating performance; Data preparation; Different data distributions; Data partition between training, test, and validation sets; Standardization; Data leakage; Data augmentation; Using data augmentation to increase the training data; Test time augmentation; Using data augmentation in deep learning libraries; Tuning hyperparameters; Grid search; Random search; Use case-using LIME for interpretability; Model interpretability with LIME; Summary; Chapter 7: Natural Language Processing Using Deep Learning | |
| 500 | |a Document classification | ||
| 520 | |a This Learning Path is your step-by-step guide to building deep learning models using R's wide range of deep learning libraries and frameworks. Through multiple real-world projects and expert guidance and tips, you'll gain the exact knowledge you need to get started with developing deep models using R. | ||
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a R (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2002004407 | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 700 | 1 | |a Wiley, Joshua F. | |
| 700 | 1 | |a Liu, Yuxi (Hayden) | |
| 700 | 1 | |a Maldonado, Pablo. | |
| 758 | |i has work: |a Deep learning with R for beginners (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGJJd6hTfFdVDy7rhrxg4y |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
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| author | Hodnett, Mark |
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| contents | Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; What is deep learning?; A conceptual overview of neural networks; Neural networks as an extension of linear regression; Neural networks as a network of memory cells; Deep neural networks; Some common myths about deep learning; Setting up your R environment; Deep learning frameworks for R; MXNet; Keras; Do I need a GPU (and what is it, anyway)?; Setting up reproducible results; Summary; Chapter 2: Training a Prediction Model; Neural networks in R Building neural network models; Generating predictions from a neural network; The problem of overfitting data -- the consequences explained; Use case -- building and applying a neural network; Summary; Chapter 3: Deep Learning Fundamentals; Building neural networks from scratch in R; Neural network web application; Neural network code; Back to deep learning; The symbol, X, y, and ctx parameters; The num.round and begin.round parameters; The optimizer parameter; The initializer parameter; The eval.metric and eval.data parameters; The epoch.end.callback parameter; The array.batch.size parameter Using regularization to overcome overfitting; L1 penalty; L1 penalty in action; L2 penalty; L2 penalty in action; Weight decay (L2 penalty in neural networks); Ensembles and model-averaging; Use case -- improving out-of-sample model performance using dropout; Summary; Chapter 4: Training Deep Prediction Models; Getting started with deep feedforward neural networks; Activation functions; Introduction to the MXNet deep learning library; Deep learning layers; Building a deep learning model; Use case -- using MXNet for classification and regression; Data download and exploration Preparing the data for our modelsThe binary classification model; The regression model; Improving the binary classification model; The unreasonable effectiveness of data; Summary; Chapter 5: Image Classification Using Convolutional Neural Networks; CNNs; Convolutional layers; Pooling layers; Dropout; Flatten layers, dense layers, and softmax; Image classification using the MXNet library; Base model (no convolutional layers); LeNet; Classification using the fashion MNIST dataset; References/further reading; Summary; Chapter 6: Tuning and Optimizing Models Evaluation metrics and evaluating performance; Types of evaluation metric; Evaluating performance; Data preparation; Different data distributions; Data partition between training, test, and validation sets; Standardization; Data leakage; Data augmentation; Using data augmentation to increase the training data; Test time augmentation; Using data augmentation in deep learning libraries; Tuning hyperparameters; Grid search; Random search; Use case-using LIME for interpretability; Model interpretability with LIME; Summary; Chapter 7: Natural Language Processing Using Deep Learning |
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| spelling | Hodnett, Mark. Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. Birmingham : Packt Publishing, Limited, 2019. 1 online resource (605 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Includes bibliographical references and index. Print version record. Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; What is deep learning?; A conceptual overview of neural networks; Neural networks as an extension of linear regression; Neural networks as a network of memory cells; Deep neural networks; Some common myths about deep learning; Setting up your R environment; Deep learning frameworks for R; MXNet; Keras; Do I need a GPU (and what is it, anyway)?; Setting up reproducible results; Summary; Chapter 2: Training a Prediction Model; Neural networks in R Building neural network models; Generating predictions from a neural network; The problem of overfitting data -- the consequences explained; Use case -- building and applying a neural network; Summary; Chapter 3: Deep Learning Fundamentals; Building neural networks from scratch in R; Neural network web application; Neural network code; Back to deep learning; The symbol, X, y, and ctx parameters; The num.round and begin.round parameters; The optimizer parameter; The initializer parameter; The eval.metric and eval.data parameters; The epoch.end.callback parameter; The array.batch.size parameter Using regularization to overcome overfitting; L1 penalty; L1 penalty in action; L2 penalty; L2 penalty in action; Weight decay (L2 penalty in neural networks); Ensembles and model-averaging; Use case -- improving out-of-sample model performance using dropout; Summary; Chapter 4: Training Deep Prediction Models; Getting started with deep feedforward neural networks; Activation functions; Introduction to the MXNet deep learning library; Deep learning layers; Building a deep learning model; Use case -- using MXNet for classification and regression; Data download and exploration Preparing the data for our modelsThe binary classification model; The regression model; Improving the binary classification model; The unreasonable effectiveness of data; Summary; Chapter 5: Image Classification Using Convolutional Neural Networks; CNNs; Convolutional layers; Pooling layers; Dropout; Flatten layers, dense layers, and softmax; Image classification using the MXNet library; Base model (no convolutional layers); LeNet; Classification using the fashion MNIST dataset; References/further reading; Summary; Chapter 6: Tuning and Optimizing Models Evaluation metrics and evaluating performance; Types of evaluation metric; Evaluating performance; Data preparation; Different data distributions; Data partition between training, test, and validation sets; Standardization; Data leakage; Data augmentation; Using data augmentation to increase the training data; Test time augmentation; Using data augmentation in deep learning libraries; Tuning hyperparameters; Grid search; Random search; Use case-using LIME for interpretability; Model interpretability with LIME; Summary; Chapter 7: Natural Language Processing Using Deep Learning Document classification This Learning Path is your step-by-step guide to building deep learning models using R's wide range of deep learning libraries and frameworks. Through multiple real-world projects and expert guidance and tips, you'll gain the exact knowledge you need to get started with developing deep models using R. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast Wiley, Joshua F. Liu, Yuxi (Hayden) Maldonado, Pablo. has work: Deep learning with R for beginners (Text) https://id.oclc.org/worldcat/entity/E39PCGJJd6hTfFdVDy7rhrxg4y https://id.oclc.org/worldcat/ontology/hasWork Print version: Hodnett, Mark. Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. Birmingham : Packt Publishing, Limited, ©2019 9781838642709 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2142582 Volltext CBO01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2142582 Volltext |
| spellingShingle | Hodnett, Mark Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: Getting Started with Deep Learning; What is deep learning?; A conceptual overview of neural networks; Neural networks as an extension of linear regression; Neural networks as a network of memory cells; Deep neural networks; Some common myths about deep learning; Setting up your R environment; Deep learning frameworks for R; MXNet; Keras; Do I need a GPU (and what is it, anyway)?; Setting up reproducible results; Summary; Chapter 2: Training a Prediction Model; Neural networks in R Building neural network models; Generating predictions from a neural network; The problem of overfitting data -- the consequences explained; Use case -- building and applying a neural network; Summary; Chapter 3: Deep Learning Fundamentals; Building neural networks from scratch in R; Neural network web application; Neural network code; Back to deep learning; The symbol, X, y, and ctx parameters; The num.round and begin.round parameters; The optimizer parameter; The initializer parameter; The eval.metric and eval.data parameters; The epoch.end.callback parameter; The array.batch.size parameter Using regularization to overcome overfitting; L1 penalty; L1 penalty in action; L2 penalty; L2 penalty in action; Weight decay (L2 penalty in neural networks); Ensembles and model-averaging; Use case -- improving out-of-sample model performance using dropout; Summary; Chapter 4: Training Deep Prediction Models; Getting started with deep feedforward neural networks; Activation functions; Introduction to the MXNet deep learning library; Deep learning layers; Building a deep learning model; Use case -- using MXNet for classification and regression; Data download and exploration Preparing the data for our modelsThe binary classification model; The regression model; Improving the binary classification model; The unreasonable effectiveness of data; Summary; Chapter 5: Image Classification Using Convolutional Neural Networks; CNNs; Convolutional layers; Pooling layers; Dropout; Flatten layers, dense layers, and softmax; Image classification using the MXNet library; Base model (no convolutional layers); LeNet; Classification using the fashion MNIST dataset; References/further reading; Summary; Chapter 6: Tuning and Optimizing Models Evaluation metrics and evaluating performance; Types of evaluation metric; Evaluating performance; Data preparation; Different data distributions; Data partition between training, test, and validation sets; Standardization; Data leakage; Data augmentation; Using data augmentation to increase the training data; Test time augmentation; Using data augmentation in deep learning libraries; Tuning hyperparameters; Grid search; Random search; Use case-using LIME for interpretability; Model interpretability with LIME; Summary; Chapter 7: Natural Language Processing Using Deep Learning Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2002004407 |
| title | Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. |
| title_auth | Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. |
| title_exact_search | Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. |
| title_full | Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. |
| title_fullStr | Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. |
| title_full_unstemmed | Deep Learning with R for Beginners : Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. |
| title_short | Deep Learning with R for Beginners : |
| title_sort | deep learning with r for beginners design neural network models in r 3 5 using tensorflow keras and mxnet |
| title_sub | Design Neural Network Models in R 3. 5 Using TensorFlow, Keras, and MXNet. |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Apprentissage automatique. R (Langage de programmation) Machine learning fast R (Computer program language) fast |
| topic_facet | Machine learning. R (Computer program language) Apprentissage automatique. R (Langage de programmation) Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2142582 |
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