Advanced Deep Learning with R

Bharatendra Rai

  • 出版商: Packt Publishing
  • 出版日期: 2019-12-16
  • 售價: $1,546
  • 貴賓價: 9.5$1,469
  • 語言: 英文
  • 頁數: 352
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1789538777
  • ISBN-13: 9781789538779
  • 相關分類: 深度學習 DeepLearning

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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.

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.

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