Advanced Deep Learning with R
Forklaring av formater
Bok med hardt omslag.
Heftet bok med mykt omslag.
Bok med tykke, stive sider.
Digitalt format. E-bok kan leses i ARK-appen eller på Kindle. Bøkene kan også lastes ned fra Din side.
Digitalt format. Nedlastbar lydbok kan lyttes til i ARK-appen. Bøkene kan også lastes ned fra Din side.
Lydbok på digikort. Krever Digispiller.
Lydbok eller musikk på CD. Krever CD-spiller eller annen kompatibel avspiller.
Vinylplate. Krever platespiller.
DVD-film. Krever DVD-spiller eller annen kompatibel avspiller.
Blu-ray-film. Krever Blu-ray-spiller eller annen kompatibel avspiller.
Kort om boken
Om Advanced Deep Learning with R
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R librariesKey FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook DescriptionDeep 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 learnLearn how to create binary and multi-class deep neural network modelsImplement GANs for generating new imagesCreate autoencoder neural networks for image dimension reduction, image de-noising and image correctionImplement deep neural networks for performing efficient text classificationLearn to define a recurrent convolutional network model for classification in KerasExplore best practices and tips for performance optimization of various deep learning modelsWho this book is forThis 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.