{"product_id":"9781789805673","title":"Deep Learning with R Cookbook : Over 45 unique recipes to delve into neural network techniques using R 3.5.x","description":"\u003cp\u003eTackle the complex challenges faced while building end-to-end deep learning models using modern R libraries\u003c\/p\u003e\n\n\u003cp\u003eKey Features\u003c\/p\u003e\n\n\u003cp\u003eUnderstand the intricacies of R deep learning packages to perform a range of deep learning tasks\n\u003cbr\u003eImplement deep learning techniques and algorithms for real-world use cases\n\u003cbr\u003eExplore various state-of-the-art techniques for fine-tuning neural network models\u003c\/p\u003e\n\n\u003cp\u003eBook 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.\u003c\/p\u003e\n\n\u003cp\u003eThe 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.\u003c\/p\u003e\n\n\u003cp\u003eBy 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\u003c\/p\u003e\n\n\u003cp\u003eWork with different datasets for image classification using CNNs\n\u003cbr\u003eApply transfer learning to solve complex computer vision problems\n\u003cbr\u003eUse RNNs and their variants such as LSTMs and Gated Recurrent Units (GRUs) for sequence data generation and classification\n\u003cbr\u003eImplement autoencoders for DL tasks such as dimensionality reduction, denoising, and image colorization\n\u003cbr\u003eBuild deep generative models to create photorealistic images using GANs and VAEs\n\u003cbr\u003eUse MXNet to accelerate the training of DL models through distributed computing\u003c\/p\u003e\n\n\u003cp\u003eWho 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.\u003c\/p\u003e","brand":"Packt Publishing Limited","offers":[{"title":"Default Title","offer_id":48222506025195,"sku":"EB_CP1000_F05-03_SIMS","price":86.13,"currency_code":"SGD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0758\/4484\/5803\/files\/9781789805673-1.jpg?v=1781704244","url":"https:\/\/kinokuniya.com.sg\/products\/9781789805673","provider":"Books Kinokuniya Singapore","version":"1.0","type":"link"}