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:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt Publishing
2019
|
| Schlagworte: | |
| Online-Zugang: | FHD01 UBY01 |
| 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: | 1 Online-Ressource (591 Seiten) |
| ISBN: | 9781838647223 |
Internformat
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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 |c Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado |
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| 505 | 8 | |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 modelsGenerating 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 overfittingL1 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 performanceTypes 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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| author | Hodnett, Mark Wiley, Joshua F. Liu, Yuxi Maldonado, Pablo |
| author_GND | (DE-588)108213497X (DE-588)1144655390 |
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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 modelsGenerating 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 overfittingL1 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 performanceTypes 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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| discipline | Informatik |
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| format | Electronic eBook |
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| spelling | Hodnett, Mark Verfasser aut Deep learning with R for beginners design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado Birmingham ; Mumbai Packt Publishing 2019 1 Online-Ressource (591 Seiten) txt rdacontent c rdamedia cr rdacarrier 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 modelsGenerating 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 overfittingL1 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 performanceTypes 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 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 R (Computer program language) Machine learning fast R (Computer program language) fast R Programm (DE-588)4705956-4 gnd rswk-swf R Programm (DE-588)4705956-4 s 1\p DE-604 Wiley, Joshua F. Verfasser (DE-588)108213497X aut Liu, Yuxi Verfasser (DE-588)1144655390 aut Maldonado, Pablo Verfasser aut Erscheint auch als Druck-Ausgabe 978-1-83864-270-9 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Hodnett, Mark Wiley, Joshua F. Liu, Yuxi Maldonado, Pablo 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 modelsGenerating 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 overfittingL1 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 performanceTypes 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 R (Computer program language) Machine learning fast R (Computer program language) fast R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4705956-4 |
| 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_exact_search_txtP | 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 Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado |
| title_fullStr | Deep learning with R for beginners design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado |
| title_full_unstemmed | Deep learning with R for beginners design neural network models in R 3.5 using TensorFlow, Keras, and MXNet Mark Hodnett, Joshua F. Wiley, Yuxi (Hayden) Liu, Pablo Maldonado |
| 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 R (Computer program language) Machine learning fast R (Computer program language) fast R Programm (DE-588)4705956-4 gnd |
| topic_facet | Machine learning R (Computer program language) R Programm |
| work_keys_str_mv | AT hodnettmark deeplearningwithrforbeginnersdesignneuralnetworkmodelsinr35usingtensorflowkerasandmxnet AT wileyjoshuaf deeplearningwithrforbeginnersdesignneuralnetworkmodelsinr35usingtensorflowkerasandmxnet AT liuyuxi deeplearningwithrforbeginnersdesignneuralnetworkmodelsinr35usingtensorflowkerasandmxnet AT maldonadopablo deeplearningwithrforbeginnersdesignneuralnetworkmodelsinr35usingtensorflowkerasandmxnet |