R Deep Learning Essentials (Paperback)

Dr. Joshua F. Wiley

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商品描述

Key Features

  • Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
  • Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
  • Build models relating to neural networks, prediction and deep prediction

Book Description

Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using model architectures. With the superb memory management and the full integration with multi-node big data platforms, the H2O engine has become more and more popular among data scientists in the field of deep learning.

This book will introduce you to the deep learning package H2O with R and help you understand the concepts of deep learning. We will start by setting up important deep learning packages available in R and then move towards building models related to neural networks, prediction, and deep prediction, all of this with the help of real-life examples.

After installing the H2O package, you will learn about prediction algorithms. Moving ahead, concepts such as overfitting data, anomalous data, and deep prediction models are explained. Finally, the book will cover concepts relating to tuning and optimizing models.

What you will learn

  • Set up the R package H2O to train deep learning models
  • Understand the core concepts behind deep learning models
  • Use Autoencoders to identify anomalous data or outliers
  • Predict or classify data automatically using deep neural networks
  • Build generalizable models using regularization to avoid overfitting the training data

About the Author

Dr. Joshua F. Wiley is a lecturer at Monash University and a senior partner at Elkhart Group Limited, a statistical consultancy. He earned his PhD from the University of California, Los Angeles. His research focuses on using advanced quantitative methods to understand the complex interplays of psychological, social, and physiological processes in relation to psychological and physical health. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. Through consulting at Elkhart Group Limited and his former work at the UCLA Statistical Consulting Group, Joshua has helped a wide array of clients, ranging from experienced researchers to biotechnology companies. He develops or codevelops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.

Table of Contents

  1. Getting Started with Deep Learning
  2. Training a Prediction Model
  3. Preventing Overfitting
  4. Identifying Anomalous Data
  5. Training Deep Prediction Models
  6. Tuning and Optimizing Models
  7. Bibliography

商品描述(中文翻譯)

主要特點


  • 利用 R 中的深度學習概念建立無監督數據的演算法能力

  • 在建立模型時,掌握常見的問題,如數據過度擬合、異常數據集、圖像識別和性能調優

  • 建立與神經網絡、預測和深度預測相關的模型

書籍描述

深度學習是基於一組演算法的機器學習分支,試圖通過使用模型架構來建模數據中的高層抽象。由於出色的內存管理和與多節點大數據平台的完全集成,H2O 引擎在深度學習領域的數據科學家中越來越受歡迎。

本書將向您介紹使用 R 中的深度學習套件 H2O,並幫助您理解深度學習的概念。我們將首先設置 R 中重要的深度學習套件,然後通過實際示例來建立與神經網絡、預測和深度預測相關的模型。

安裝 H2O 套件後,您將學習預測演算法。隨著進一步的學習,將解釋過度擬合數據、異常數據和深度預測模型等概念。最後,本書將涵蓋調優和優化模型的概念。

您將學到什麼


  • 設置 R 套件 H2O 以訓練深度學習模型

  • 理解深度學習模型的核心概念

  • 使用自編碼器識別異常數據或離群值

  • 使用深度神經網絡自動預測或分類數據

  • 使用正則化建立具有泛化能力的模型,以避免過度擬合訓練數據

關於作者

Joshua F. Wiley 博士是蒙納士大學的講師,也是統計諮詢公司 Elkhart Group Limited 的高級合夥人。他在加州大學洛杉磯分校獲得博士學位。他的研究重點是使用先進的定量方法來理解心理、社會和生理過程與心理和身體健康之間的複雜相互作用。在統計學和數據科學方面,Joshua 專注於生物統計學,並對可重複研究和數據和統計模型的圖形顯示感興趣。通過在 Elkhart Group Limited 的諮詢工作以及他在 UCLA 統計諮詢組的前任工作,Joshua 幫助了各種各樣的客戶,從經驗豐富的研究人員到生物技術公司。他開發或共同開發了多個 R 套件,包括 varian,一個用於進行貝葉斯比例-位置結構方程模型的套件,以及 MplusAutomation,一個將 R 與商業 Mplus 軟件鏈接起來的熱門套件。

目錄


  1. 深度學習入門

  2. 訓練預測模型

  3. 防止過度擬合

  4. 識別異常數據

  5. 訓練深度預測模型

  6. 調優和優化模型

  7. 參考文獻