Advanced Deep Learning with R
暫譯: 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**

- 實現深度學習算法，利用技巧和竅門構建 AI 模型
- 使用專家技術理解深度學習模型的運作方式
- 應用強化學習、計算機視覺、生成對抗網絡 (GANs) 和自然語言處理 (NLP)，使用各種數據集

**書籍描述**

深度學習是機器學習的一個分支，基於一組算法，試圖對數據中的高層次抽象進行建模。《進階深度學習與 R》將幫助您理解流行的深度學習架構及其變體，並提供實際案例。

這本深度學習書籍首先涵蓋了預測和分類所需的基本深度學習技術和概念。您將學習神經網絡、深度學習架構以及使用 R 實現深度學習的基本原理。本書還將指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算法。您將通過進階範例快速掌握人工神經網絡、遞歸神經網絡、卷積神經網絡、長短期記憶網絡等。之後，您將發現如何應用生成對抗網絡 (GANs) 生成新圖像；使用自編碼神經網絡進行圖像維度縮減、圖像去噪和圖像修正，以及轉移學習來準備、定義、訓練和建模深度神經網絡。

在本書結束時，您將能夠利用實際案例，將您的知識和新獲得的技能應用於 R 中的深度學習算法。

**您將學到的內容**

- 學習如何創建二元和多類深度神經網絡模型
- 實現 GANs 以生成新圖像
- 創建自編碼神經網絡以進行圖像維度縮減、圖像去噪和圖像修正
- 實現深度神經網絡以進行高效的文本分類
- 學習在 Keras 中定義遞歸卷積網絡模型以進行分類
- 探索各種深度學習模型性能優化的最佳實踐和技巧

**本書適合誰**

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 AI 愛好者，旨在幫助他們發展技能和知識，利用 R 的力量實現深度學習技術和算法。需要對機器學習有扎實的理解，並具備 R 程式語言的工作知識。

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 是美國馬薩諸塞州達特茅斯的查爾頓商學院商業分析的主席及教授，並擔任科技管理碩士學位課程的主任。他在底特律的韋恩州立大學獲得工業工程博士學位，並在印度統計學院獲得質量、可靠性及運籌學碩士學位。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已在超過198個國家觀看。他在數據科學、機器學習和供應鏈管理等領域擁有超過20年的諮詢和培訓經驗，涵蓋軟體、汽車、電子、食品、化學等行業。

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)


  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

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