Advanced Deep Learning with R (進階深度學習與R)

Bharatendra Rai

Advanced Deep Learning with R
  • 出版商: Packt Publishing
  • 出版日期: 2019-12-16
  • 售價: $1,480
  • 貴賓價: 9.5 折 $1,406
  • 語言: 英文
  • 頁數: 352
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789538777
  • ISBN-13: 9781789538779
  • 相關分類: DeepLearning
  • 相關翻譯: 基於R語言的高級深度學習 (簡中版)

    • 立即出貨 (庫存=1)

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商品描述

Key Features

  • Implement deep learning algorithms to build AI models with the help of tips and tricks
  • Understand how deep learning models operate using expert techniques
  • Apply reinforcement learning, computer vision, GANs, and NLP using a range of datasets

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. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them.

This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network.

By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples.

What you will learn

  • Learn how to create binary and multi-class deep neural network models
  • Implement GANs for generating new images
  • Create autoencoder neural networks for image dimension reduction, image de-noising and image correction
  • Implement deep neural networks for performing efficient text classification
  • Learn to define a recurrent convolutional network model for classification in Keras
  • Explore best practices and tips for performance optimization of various deep learning models

Who this book is for

This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.

商品描述(中文翻譯)

主要特點


  • 使用專業技巧實現深度學習演算法，建立人工智慧模型

  • 透過專家技巧了解深度學習模型的運作方式

  • 應用增強學習、電腦視覺、生成對抗網路（GANs）和自然語言處理（NLP）於各種數據集

書籍描述

深度學習是基於一組演算法的機器學習分支，試圖對數據中的高層抽象進行建模。《Advanced Deep Learning with R》將幫助您了解流行的深度學習架構及其在R中的變體，並提供實際示例。

本書首先介紹了用於預測和分類的基本深度學習技術和概念。您將學習神經網絡、深度學習架構以及在R中實現深度學習的基礎知識。本書還將引導您使用重要的深度學習庫，如Keras-R和TensorFlow-R，在應用中實現深度學習演算法。通過高級示例，您將深入了解人工神經網絡、循環神經網絡、卷積神經網絡、長短期記憶網絡等。隨後，您將發現如何應用生成對抗網絡（GANs）生成新圖像，使用自編碼器神經網絡進行圖像尺寸縮減、圖像去噪和圖像校正，以及使用遷移學習準備、定義、訓練和建模深度神經網絡。

通過閱讀本書，您將準備好通過實際示例在R中應用深度學習演算法。

你將學到什麼


  • 學習如何創建二元和多類深度神經網絡模型

  • 實現生成新圖像的生成對抗網絡（GANs）

  • 創建自編碼器神經網絡，進行圖像尺寸縮減、圖像去噪和圖像校正

  • 實現深度神經網絡進行高效的文本分類

  • 學習在Keras中定義循環卷積網絡模型進行分類

  • 探索各種深度學習模型的性能優化的最佳實踐和技巧

適合閱讀對象

本書適合數據科學家、機器學習從業者、深度學習研究人員和人工智慧愛好者，他們希望通過R的強大功能來開發深度學習技術和演算法的技能和知識。需要具備扎實的機器學習基礎和R編程語言的工作知識。

作者簡介

Bharatendra Rai is a chairperson and professor of business analytics, and the director of the Master of Science in Technology Management program at the Charlton College of Business at UMass Dartmouth. He received a Ph.D. in industrial engineering from Wayne State University, Detroit. He received a master's in quality, reliability, and OR from Indian Statistical Institute, India. His current research interests include machine learning and deep learning applications. His deep learning lecture videos on YouTube are watched in over 198 countries. He has over 20 years of consulting and training experience in industries such as software, automotive, electronics, food, chemicals, and so on, in the areas of data science, machine learning, and supply chain management.

作者簡介(中文翻譯)

Bharatendra Rai是UMass Dartmouth Charlton商學院的商業分析主席和教授，也是科技管理碩士課程的主任。他在Wayne State University取得工業工程博士學位，並在印度統計學研究所獲得品質、可靠性和運籌學碩士學位。他目前的研究興趣包括機器學習和深度學習應用。他在YouTube上的深度學習講座視頻在198個國家觀看。他在軟件、汽車、電子、食品、化學等行業擁有超過20年的咨詢和培訓經驗，涉及數據科學、機器學習和供應鏈管理等領域。

目錄大綱

  1. Revisiting Deep Learning architecture and techniques
  2. Deep Neural Networks for multiclass classification
  3. Deep Neural Networks for regression
  4. Image classification and recognition
  5. Image classification using convolutional neural networks
  6. Applying Autoencoder neural networks using Keras
  7. Image classification for small data using transfer learning
  8. Creating new images using generative adversarial networks
  9. Deep network for text classification
  10. Text classification using recurrent neural networks
  11. Text classification using Long Short-Term Memory Network
  12. Text classification using convolutional recurrent networks
  13. Tips, tricks and the road ahead

目錄大綱(中文翻譯)

- 重新探討深度學習架構和技術
- 應用於多類別分類的深度神經網絡
- 應用於回歸的深度神經網絡
- 圖像分類和識別
- 使用卷積神經網絡進行圖像分類
- 使用Keras應用自編碼器神經網絡
- 使用遷移學習進行小數據的圖像分類
- 使用生成對抗網絡創建新圖像
- 用於文本分類的深度網絡
- 使用循環神經網絡進行文本分類
- 使用長短期記憶網絡進行文本分類
- 使用卷積循環網絡進行文本分類
- 小貼士、技巧和未來的發展方向

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