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Deep Learning with R Cookbook (eBook)

Over 45 unique recipes to delve into neural network techniques using R 3.5.x
eBook Download: EPUB
2020
328 Seiten
Packt Publishing (Verlag)
978-1-78980-827-8 (ISBN)

Lese- und Medienproben

Deep Learning with R Cookbook - Swarna Gupta, Rehan Ali Ansari, Dipayan Sarkar
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Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries




Key Features



  • Understand the intricacies of R deep learning packages to perform a range of deep learning tasks


  • Implement deep learning techniques and algorithms for real-world use cases


  • Explore various state-of-the-art techniques for fine-tuning neural network models



Book Description



Deep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques.







The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You'll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you'll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you'll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps.







By the end of this book, you'll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems.




What you will learn



  • Work with different datasets for image classification using CNNs


  • Apply transfer learning to solve complex computer vision problems


  • Use RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification


  • Implement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization


  • Build deep generative models to create photorealistic images using GANs and VAEs


  • Use MXNet to accelerate the training of DL models through distributed computing



Who this book is for



This deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.


Tackle the complex challenges faced while building end-to-end deep learning models using modern R librariesKey FeaturesUnderstand the intricacies of R deep learning packages to perform a range of deep learning tasksImplement deep learning techniques and algorithms for real-world use casesExplore various state-of-the-art techniques for fine-tuning neural network modelsBook DescriptionDeep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning. This book will get you up and running with R 3.5.x to help you implement DL techniques.The book starts with the various DL techniques that you can implement in your apps. A unique set of recipes will help you solve binomial and multinomial classification problems, and perform regression and hyperparameter optimization. To help you gain hands-on experience of concepts, the book features recipes for implementing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Long short-term memory (LSTMs) networks, as well as sequence-to-sequence models and reinforcement learning. You'll then learn about high-performance computation using GPUs, along with learning about parallel computation capabilities in R. Later, you'll explore libraries, such as MXNet, that are designed for GPU computing and state-of-the-art DL. Finally, you'll discover how to solve different problems in NLP, object detection, and action identification, before understanding how to use pre-trained models in DL apps.By the end of this book, you'll have comprehensive knowledge of DL and DL packages, and be able to develop effective solutions for different DL problems.What you will learnWork with different datasets for image classification using CNNsApply transfer learning to solve complex computer vision problemsUse RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classificationImplement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorizationBuild deep generative models to create photorealistic images using GANs and VAEsUse MXNet to accelerate the training of DL models through distributed computingWho this book is forThis deep learning book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to learn key tasks in deep learning domains using a recipe-based approach. A strong understanding of machine learning and working knowledge of the R programming language is mandatory.
Erscheint lt. Verlag 21.2.2020
Sprache englisch
Themenwelt Mathematik / Informatik Informatik Theorie / Studium
Schlagworte algorithms • Deep learning • Deep Reinforcement Learning • face recognition • GaN • GPU • image classification • LSTM • neural network • NLP • Object detection • R • R 3.X • Regression • Reinforcement Learning • RNN • Speech • Text Summarization • variational autoencoders
ISBN-10 1-78980-827-8 / 1789808278
ISBN-13 978-1-78980-827-8 / 9781789808278
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Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belle­tristik und Sach­büchern. Der Fließ­text wird dynamisch an die Display- und Schrift­größe ange­passt. Auch für mobile Lese­geräte ist EPUB daher gut geeignet.

Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür die kostenlose Software Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür eine kostenlose App.
Geräteliste und zusätzliche Hinweise

Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.

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