| Description |
1 online resource (605 pages) |
| Physical Medium |
polychrome |
| Description |
text file |
| Bibliography |
Includes bibliographical references and index. |
| 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 |
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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 |
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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 |
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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 |
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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 |
| Note |
Document classification |
| Summary |
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. |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Machine learning.
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Machine learning. |
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R (Computer program language)
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R (Computer program language) |
| Genre/Form |
Electronic books.
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| Added Author |
Wiley, Joshua F.
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Liu, Yuxi (Hayden)
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Maldonado, Pablo.
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| Other Form: |
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 |
| ISBN |
1838647228 |
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9781838647223 (electronic book) |
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