R Deep Learning Projects: Master the techniques to train and deploy neural networks in R

Yuxi (Hayden) Liu, Pablo Maldonado

  • 出版商: Packt Publishing
  • 出版日期: 2018-02-23
  • 定價: $1,200
  • 售價: 8.0$960
  • 語言: 英文
  • 頁數: 258
  • 裝訂: Paperback
  • ISBN: 1788478401
  • ISBN-13: 9781788478403
  • 相關分類: R 語言DeepLearning
  • 立即出貨 (庫存=1)

商品描述

5 real -world projects to help you master the concepts of deep learning

Key Features

  • Master the different deep learning paradigms and build real-world projects related to Text Generation, Sentiment Analysis, Fraud Detection, and more
  • Get to grips with R's impressive range of Deep Learning libraries and frameworks like deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
  • Practical projects that show you how to implement different neural networks with helpful tips, tricks and best practices

Book Description

R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is one of the popularly used languages for deep learning. Deep Learning, as we all know is one of the trending topics today - and is finding practical applications in a lot of domains.

This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll see how to train effective neural networks in R - including convolutional neural networks, recurrent neural networks and LSTMs - and apply them in practical scenarios. The book also highlights how the neural networks can be trained using the capabilities of the GPU. You will use popular R libraries and packages such as MXNetR, H2O, deepnet and more to implement the projects.

By the end of this book, you will have a better understanding of the deep learning concepts and techniques and how to use them in a practical setting.

What You Will Learn

  • Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec
  • Apply neural networks to perform handwritten digit recognition using MXNet
  • Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic signs classification
  • Implement Credit Card Fraud Detection with Autoencoders
  • Be a Maestro in reconstructing images using Variational autoencoders
  • Wade through Sentiment Analysis from movie reviews
  • Run from past to future and vice versa with Bidirectional Long Short-Term Memory (LSTM) networks
  • Understand the applications of Autoencoder Neural Networks in Clustering and Dimensionality Reduction

Who This Book Is For

Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book to be a useful resource. Knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.

商品描述(中文翻譯)

《5個真實世界專案,幫助您掌握深度學習概念》

關鍵特點:
- 掌握不同的深度學習範式,並建立與文本生成、情感分析、詐騙檢測等相關的真實世界專案
- 熟悉R強大的深度學習函式庫和框架,如deepnet、MXNetR、Tensorflow、H2O、Keras和text2vec
- 實用專案示範,展示如何實現不同的神經網絡,並提供實用技巧、訣竅和最佳實踐

書籍描述:
R是統計學家和數學家用於統計分析的流行程式語言,也是深度學習中常用的程式語言之一。深度學習是當今熱門話題之一,並在許多領域找到實際應用。

本書演示了五個真實世界專案的端到端實現,涵蓋深度學習中的流行主題,如手寫數字識別、交通信號檢測、詐騙檢測、文本生成和情感分析。您將學習如何在R中訓練有效的神經網絡,包括卷積神經網絡、循環神經網絡和LSTM,並將它們應用於實際情境中。本書還強調了如何使用GPU的能力來訓練神經網絡。您將使用流行的R函式庫和套件,如MXNetR、H2O、deepnet等來實現這些專案。

通過閱讀本書,您將更好地理解深度學習的概念和技術,以及如何在實際環境中應用它們。

學到什麼:
- 使用deepnet、MXNetR、Tensorflow、H2O、Keras和text2vec等套件來優化深度學習模型
- 使用MXNet進行手寫數字識別
- 使用CNN模型、神經網絡API、Keras和TensorFlow進行交通信號分類
- 使用自編碼器實現信用卡詐騙檢測
- 使用變分自編碼器重建圖像
- 進行情感分析,從電影評論中獲取情感
- 使用雙向LSTM網絡在過去和未來之間進行預測
- 了解自編碼器神經網絡在聚類和降維中的應用

適合對象:
- 機器學習專業人士和數據科學家,希望通過在R中實施實際專案來掌握深度學習的知識
- 需要具備R程式設計和深度學習基本概念的讀者,才能充分利用本書的內容。