R Deep Learning Essentials - Second Edition: A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet

Mark Hodnett, Joshua F. Wiley

  • 出版商: Packt Publishing
  • 出版日期: 2018-08-22
  • 售價: $1,380
  • 貴賓價: 9.5$1,311
  • 語言: 英文
  • 頁數: 378
  • 裝訂: Paperback
  • ISBN: 178899289X
  • ISBN-13: 9781788992893
  • 相關分類: DeepLearningR 語言TensorFlow
  • 立即出貨 (庫存=1)

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商品描述

Implementing neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Key Features

  • Use R 3.5 for building deep learning models for computer vision, text and more
  • Apply deep learning techniques in the cloud for large-scale processing
  • Build, train and optimize neural network models on a range of datasets

Book Description

Deep Learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing. This book will open the gates for you to enter the world of neural networks by building powerful deep learning models using R ecosystem.

This book will introduce deep learning fundamentals from first principles, you will learn how to build a neural network model from scratch. It will show how to use deep learning libraries such as Keras, MXNet and Tensorflow. We will build deep learning models for a variety of tasks and problems including structured data, computer vision, text data, anomaly detection and recommendation systems. Later we will cover advanced topics such as Generative Adversarial Networks, Transfer Learning, large-scale deep learning in the cloud. You will also learn about the theoretical concepts of deep learning projects such as how to tune and optimize your model, how to deal with overfitting, data augmentation, and other advanced topics.

By the end of this book, you will be all ready to implement deep learning concepts in research work or projects.

What you will learn

  • Use deep learning libraries such as Keras, Tensorflow, and MXNet in R
  • Build shallow neural network prediction models
  • Prevent models from overfitting the data to improve generalizability
  • Techniques for finding the best hyperparameters for deep learning models
  • Build Natural Language Processing models using Keras and TensorFlow in R
  • Learn how to use deep learning for computer vision tasks
  • Implement deep learning topics such as GANs, auto-encoders, and transfer learning

Who This Book Is For

This book caters to aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. You should have a fundamental understanding of the R language to get the most out of the book.

商品描述(中文翻譯)

在 R 3.5 中使用 TensorFlow、Keras 和 MXNet 實現神經網絡模型

主要特點:
- 使用 R 3.5 構建計算機視覺、文本等深度學習模型
- 在雲端中應用深度學習技術進行大規模處理
- 在各種數據集上構建、訓練和優化神經網絡模型

書籍描述:
深度學習是機器學習的一個強大子集,在計算機視覺和自然語言處理等領域非常成功。本書將通過使用 R 生態系統構建強大的深度學習模型,為您打開神經網絡世界的大門。

本書將從基本原理介紹深度學習基礎知識,您將學習如何從頭開始構建神經網絡模型。它將展示如何使用 Keras、MXNet 和 TensorFlow 等深度學習庫。我們將為各種任務和問題構建深度學習模型,包括結構化數據、計算機視覺、文本數據、異常檢測和推薦系統。之後,我們將涵蓋高級主題,如生成對抗網絡、遷移學習和雲端中的大規模深度學習。您還將學習有關深度學習項目的理論概念,例如如何調整和優化模型、如何處理過擬合、數據擴增和其他高級主題。

通過閱讀本書,您將準備好在研究工作或項目中實施深度學習概念。

您將學到:
- 在 R 中使用 Keras、TensorFlow 和 MXNet 等深度學習庫
- 構建淺層神經網絡預測模型
- 避免模型過擬合以提高泛化能力
- 尋找深度學習模型的最佳超參數技術
- 使用 Keras 和 TensorFlow 在 R 中構建自然語言處理模型
- 學習如何在計算機視覺任務中使用深度學習
- 實現生成對抗網絡、自編碼器和遷移學習等深度學習主題

適合對象:
本書適合有志成為數據科學家、數據分析師、機器學習開發人員和深度學習愛好者的讀者,他們對機器學習概念有基本的理解,並希望使用 R 探索深度學習範式。您應該對 R 語言有基本的理解,以充分利用本書。