Hands-On Deep Learning with R
暫譯: 實戰深度學習與 R

Michael Pawlus , Rodger Devine

Hands-On Deep Learning with R
  • 出版商: Packt Publishing
  • 出版日期: 2020-04-24
  • 售價: $1,830
  • 貴賓價: 9.5 折 $1,739
  • 語言: 英文
  • 頁數: 330
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1788996836
  • ISBN-13: 9781788996839
  • 相關分類: DeepLearning

    • 海外代購書籍(需單獨結帳)

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商品描述

Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and Deepnet

Key Features

  • Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem
  • Improve models using parameter tuning, feature engineering, and ensembling
  • Apply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domains

Book Description

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming.

This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You'll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you'll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems.

By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.

What you will learn

  • Design a feedforward neural network to see how the activation function computes an output
  • Create an image recognition model using convolutional neural networks (CNNs)
  • Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm
  • Apply text cleaning techniques to remove uninformative text using NLP
  • Build, train, and evaluate a GAN model for face generation
  • Understand the concept and implementation of reinforcement learning in R

Who this book is for

This book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.

商品描述(中文翻譯)

**探索並實現深度學習以解決各種現實世界的問題，使用現代 R 函式庫如 TensorFlow、MXNet、H2O 和 Deepnet**

#### 主要特點

- 理解使用 R 的深度學習演算法和架構，並確定哪種演算法最適合特定問題
- 通過參數調整、特徵工程和集成來改善模型
- 在不同領域應用先進的神經網絡模型，如深度自編碼器和生成對抗網絡（GANs）

#### 書籍描述

深度學習使得從大量數據中高效且準確地學習成為可能。本書將幫助您克服使用各種深度學習演算法和架構的挑戰，並使用 R 程式設計。

本書首先簡要概述機器學習和深度學習，以及如何構建您的第一個神經網絡。您將理解各種深度學習演算法的架構及其適用領域，學習如何構建深度學習模型、優化超參數和評估模型性能。本書還將涵蓋在圖像處理、自然語言處理（NLP）、推薦系統和預測分析中的各種深度學習應用。後面的章節將展示如何解決識別問題，如圖像識別和信號檢測，程式化地總結文檔，進行主題建模，以及預測股市價格。在書的最後部分，您將學習 GANs 的常見應用以及如何使用它們構建面部生成模型。最後，您將掌握使用強化學習和深度強化學習來解決各種現實世界問題的方法。

在本深度學習書籍結束時，您將能夠使用適當的框架和演算法構建和部署自己的深度學習應用。

#### 您將學到什麼

- 設計一個前饋神經網絡，以了解激活函數如何計算輸出
- 使用卷積神經網絡（CNNs）創建圖像識別模型
- 準備數據，決定隱藏層和神經元，並使用反向傳播演算法訓練您的模型
- 應用文本清理技術，使用 NLP 移除無信息的文本
- 構建、訓練和評估一個用於面部生成的 GAN 模型
- 理解強化學習的概念及其在 R 中的實現

#### 本書適合誰

本書適合數據科學家、機器學習工程師和熟悉機器學習的深度學習開發者，旨在通過實際範例增強他們對深度學習的知識。任何希望提高其機器學習應用效率並探索 R 中各種選項的人也會發現本書有用。預期讀者具備基本的機器學習技術知識和 R 程式設計語言的工作知識。

作者簡介

Michael Pawlus is a data scientist at The Ohio State University where he is currently part of the team building of the data science infrastructure for the Advancement department while also leading the implementation of innovative projects there. Prior to this, Michael was a data scientist at the University of Southern California. In addition to this work, Michael has chaired data science education conferences, published articles on the role of data science within fundraising and currently serves on committees where he is focused on providing a wider variety of educational offerings as well as increasing the diversity of content creators in this space. Michael holds degrees from Grand Valley State University and the University of Sheffield.

Rodger Devine is the Associate Dean of External Affairs for Strategy and Innovation at the USC Dornsife College of Letters, Arts, and Sciences. Rodger's portfolio includes advancement operations, BI, leadership annual giving, program innovation, prospect development, and strategic information management. Prior to USC, Rodger served as the Director of Information, Analytics, and Annual Giving at the Michigan Ross School of Business. Rodger brings nearly 20 years of experience in software engineering, IT operations, BI, project management, organizational development, and leadership. Rodger completed his Masters in data science at the University of Michigan and is a doctoral student in the OCL program at the USC Rossier School of Education.

作者簡介(中文翻譯)

邁克爾·帕盧斯是俄亥俄州立大學的數據科學家，目前他是推進部門數據科學基礎設施建設團隊的一部分，同時也負責那裡創新項目的實施。在此之前，邁克爾曾是南加州大學的數據科學家。除了這項工作，邁克爾還主持過數據科學教育會議，發表過有關數據科學在籌款中角色的文章，並且目前在一些委員會中任職，專注於提供更廣泛的教育產品以及增加該領域內容創作者的多樣性。邁克爾擁有大峽谷州立大學和謝菲爾德大學的學位。

羅傑·德維恩是南加州大學多恩斯法學院的外部事務副院長，負責策略與創新。羅傑的工作範疇包括推進運營、商業智慧（BI）、領導年度捐贈、項目創新、潛在捐贈者開發和戰略信息管理。在加入南加州大學之前，羅傑曾擔任密西根大學羅斯商學院的信息、分析和年度捐贈主任。羅傑擁有近20年的軟體工程、IT運營、商業智慧、項目管理、組織發展和領導經驗。他在密西根大學獲得數據科學碩士學位，並且是南加州大學羅斯教育學院OCL項目的博士生。

目錄大綱

  1. Machine Learning Basics
  2. Setting Up R for Deep Learning
  3. Artificial Neural Networks
  4. Convolutional Neural Networks for Image Recognition
  5. Multilayer Perceptron Neural Networks for Signal Detection
  6. Neural Collaborative Filtering Using Embeddings
  7. Deep Learning for Natural Language Processing
  8. Long Short-Term Memory Networks for Stock Forecast
  9. Generative Adversarial Networks for Face Generation
  10. Reinforcement Learning for gaming
  11. Deep Q Learning for Maze Solving

目錄大綱(中文翻譯)


  1. Machine Learning Basics

  2. Setting Up R for Deep Learning

  3. Artificial Neural Networks

  4. Convolutional Neural Networks for Image Recognition

  5. Multilayer Perceptron Neural Networks for Signal Detection

  6. Neural Collaborative Filtering Using Embeddings

  7. Deep Learning for Natural Language Processing

  8. Long Short-Term Memory Networks for Stock Forecast

  9. Generative Adversarial Networks for Face Generation

  10. Reinforcement Learning for gaming

  11. Deep Q Learning for Maze Solving

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