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Hands-on deep learning with R : a practical guide to designing, building, and improving neural network models using R
Pawlus, Michael
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | Hands-on deep learning with R : a practical guide to designing, building, and improving neural network models using R/ Michael Pawlus, Rodger Devine. |
| 개인저자 | Pawlus, Michael,author. Devine, Rodger,author, |
| 발행사항 | Birmingham, UK: Packt Publishing, 2020. |
| 형태사항 | 1 online resource (1 volume): illustrations. |
| 기타형태 저록 | Print version: Pawlus, Michael Hands-On Deep Learning with R : A Practical Guide to Designing, Building, and Improving Neural Network Models Using R Birmingham : Packt Publishing, Limited,c2020 |
| ISBN | 9781788993784 1788993780 |
| 서지주기 | Includes bibliographical references. |
| 내용주기 | Cover -- Title Page -- Copyright and Credits -- Dedication -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Deep Learning Basics -- Chapter 1: Machine Learning Basics -- An overview of machine learning -- Preparing data for modeling -- Handling missing values -- Training a model on prepared data -- Train and test data -- Choosing an algorithm -- Evaluating model results -- Machine learning metrics -- Improving model results -- Reviewing different algorithms -- Summary -- Chapter 2: Setting Up R for Deep Learning -- Technical requirements -- Installing the packages Installing ReinforcementLearning -- Installing RBM -- Installing Keras -- Installing H2O -- Installing MXNet -- Preparing a sample dataset -- Exploring Keras -- Available functions -- A Keras example -- Exploring MXNet -- Available functions -- Getting started with MXNet -- Exploring H2O -- Available functions -- An H2O example -- Exploring ReinforcementLearning and RBM -- Reinforcement learning example -- An RBM example -- Comparing the deep learning libraries -- Summary -- Chapter 3: Artificial Neural Networks -- Technical requirements -- Contrasting deep learning with machine learning Comparing neural networks and the human brain -- Utilizing bias and activation functions within hidden layers -- Surveying activation functions -- Exploring the sigmoid function -- Investigating the hyperbolic tangent function -- Plotting the rectified linear units activation function -- Calculating the Leaky ReLU activation function -- Defining the swish activation function -- Predicting class likelihood with softmax -- Creating a feedforward network -- Writing a neural network with Base R -- Creating a model with Wisconsin cancer data -- Augmenting our neural network with backpropagation Deciding on the hidden layers and neurons -- Training and evaluating the model -- Summary -- Chapter 6: Neural Collaborative Filtering Using Embeddings -- Technical requirements -- Introducing recommender systems -- Collaborative filtering with neural networks -- Exploring embeddings -- Preparing, preprocessing, and exploring data -- Performing exploratory data analysis -- Creating user and item embeddings -- Building and training a neural recommender system -- Evaluating results and tuning hyperparameters -- Hyperparameter tuning -- Adding dropout layers -- Adjusting for user-item bias |
| 요약 | Section 2: Deep Learning Applications -- Chapter 4: CNNs for Image Recognition -- Technical requirements -- Image recognition with shallow nets -- Image recognition with convolutional neural networks -- Optimizers -- Loss functions -- Evaluation metrics -- Enhancing the model with additional layers -- Choosing the most appropriate activation function -- Selecting optimal epochs using dropout and early stopping -- Summary -- Chapter 5: Multilayer Perceptron for Signal Detection -- Technical requirements -- Understanding multilayer perceptrons -- Preparing and preprocessing data |
| 요약 | Deep learning enables efficient and accurate learning from data. Developers working with R will be able to put their knowledge to work with this practical guide to deep learning. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time. |
| 일반주제명 | R (Computer program language) Machine learning. Neural networks (Computer science) Application software --Development. Data capture & analysis. Mathematical theory of computation. Machine learning. Artificial intelligence. Computers --Data Processing. Computers --Machine Theory. Computers --Intelligence (AI) & Semantics. Application software --Development. Machine learning. Neural networks (Computer science) R (Computer program language) |
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