الرئيسية

الكتب

التكنولوجيا والكمبيوتر

علوم الحاسب

الذكاء الاصطناعي والتعلم الآلي

Deep Learning with R for Beginners: Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet

210.00

شامل ضريبة القيمة المضافة

توصيل مجاني

احصل عليه خلال 26 - 28 يونيو

اطلب في غضون 23 ساعة 48 دقيقة

التوصيل بواسطة نوون

البائع ذو تقييم عالي

الدفع عند الاستلام

عملية تحويل آمنة

عروض البنوك

احصل على 5% رصيد مسترجع باستخدام بطاقة بنك المشرق نون الائتمانية. اشترك الآن.

البائع

يتم البيع عبر

بوكسمارت

4.0

62% تقييم إيجابي للبائع

المنتج كما في الوصف

70%

شريك لنون منذ

7+ سنة

أحدث التقييمات الإيجابية

توصيل مجاني لنقطة نون ومراكز الاستلام

هذا المنتج قابل للإرجاع مجاناً

نظرة عامة على المنتج

المواصفات

الناشر	Packt Publishing
رقم الكتاب المعياري الدولي 13	9781838642709
رقم الكتاب المعياري الدولي 10	1838642706
الكاتب	Mark Hodnett
اللغة	English

وصف الكتاب	Explore the world of neural networks by building powerful deep learning models using the R ecosystemKey Features: - Get to grips with the fundamentals of deep learning and neural networks - Use R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processing - Implement effective deep learning systems in R with the help of end-to-end projects Book Description: Deep learning has a range of practical applications in several domains, while R is the preferred language for designing and deploying deep learning models.This Learning Path introduces you to the basics of deep learning and even teaches you to build a neural network model from scratch. As you make your way through the chapters, you'll explore deep learning libraries and understand how to create deep learning models for a variety of challenges, right from anomaly detection to recommendation systems. The Learning Path will then help you cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud, in addition to model optimization, overfitting, and data augmentation. Through real-world projects, you'll also get up to speed with training convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs) in R.By the end of this Learning Path, you'll be well-versed with deep learning and have the skills you need to implement a number of deep learning concepts in your research work or projects.What You Will Learn: - Implement credit card fraud detection with autoencoders - Train neural networks to perform handwritten digit recognition using MXNet - Reconstruct images using variational autoencoders - Explore the applications of autoencoder neural networks in clustering and dimensionality reduction - Create natural language processing (NLP) models using Keras and TensorFlow in R - Prevent models from overfitting the data to improve generalizability - Build shallow neural network prediction models Who this book is for: This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. A fundamental understanding of R programming and familiarity with the basic concepts of deep learning are necessary to get the most out of this Learning Path.
عن المؤلف	Mark Hodnett is a data scientist with over 20 years of industry experience in software development, business intelligence systems, and data science. He has worked in a variety of industries, including CRM systems, retail loyalty, IoT systems, and accountancy. He holds a master's in data science and an MBA. He works in Cork, Ireland, as a senior data scientist with AltViz.Joshua F. Wiley is a lecturer at Monash University, conducting quantitative research on sleep, stress, and health.. He earned his Ph.D. from the University of California, Los Angeles and completed post doctoral training in primary care and prevention. In statistics and data science, Joshua focuses on biostatistics and is interested in reproducible research and graphical displays of data and statistical models. He develops or co-develops a number of R packages including varian, a package to conduct Bayesian scale-location structural equation models, and MplusAutomation, a popular package that links R to the commercial Mplus software.Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his expertise in reinforcement learning to computational. He is an education enthusiast and the author of a series of ML books. His first book, Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt. He also published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.Pablo Maldonado is an applied mathematician and data scientist with a taste for software development since his days of programming BASIC on a Tandy 1000. As an academic and business consultant, he spends a great deal of his time building applied artificial intelligence solutions for text analytics, sensor and transactional data, and reinforcement learning. Pablo earned his Ph.D. in applied mathematics (with focus on mathematical game theory) at the Universite Pierre et Marie Curie in Paris, France.
تاريخ النشر	17 May 2019
عدد الصفحات	612 pages

مجموع السلة  210.00