Deep Learning For Business With R
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Deep Learning For Business With R
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Author : N. Lewis
language : en
Publisher:
Release Date : 2016-08-31
Deep Learning For Business With R written by N. Lewis and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-31 with categories.
Master Deep Learning & Leverage Business Analytics - the Easy Way! Deep Learning for Business With R takes you on a gentle, fun and unhurried journey to building your own deep neural network models for business use in R. Using plain language, it offers an intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. BUSINESS ANALYTICS FAST! This book is an ideal introduction to deep learning for business analytics. It is designed to be accessible. It will teach you, in simple and easy-to-understand terms, how to take advantage of deep learning to enhance business outcomes. NO EXPERIENCE REQUIRED I'm assuming you never did like linear algebra, don't want to see things derived, dislike complicated computer code, and you're here because you want to see how to use deep neural networks for business problems explained in plain language, and try them out for yourself. THIS BOOK IS FOR YOU IF YOU WANT: Explanations rather than mathematical derivation Real world applications that make sense. Illustrations to deepen your understanding. Worked examples in R you can easily follow and immediately implement. Ideas you can actually use and try on your own data. QUICK AND EASY: Deep Learning is little more than using straight-forward steps to process data into actionable insight. And in Deep Learning for Business with R, author Dr. N.D Lewis will show you how that's done. It's easier than you think. Through a simple to follow process you will learn how to build deep neural network models for business problems in R. Once you have mastered the process, it will be easy for you to translate your knowledge into your own powerful business applications. TAKE THE SHORTCUT: R is easy to use, available on all major operating systems and free! Each chapter covers, step by step, a different aspect of deep neural networks. You get your hands dirty as you work through some challenging real world business issues. YOU'LL LEARN HOW TO: Unleash the power of Deep Neural Networks for classifying the popularity of online news stories.. Develop hands on solutions for assessing customer churn.. Design successful applications for modeling customer brand choice. Master techniques for efficient product demand forecasting. Deploy deep neural networks to predict credit card expenditure. Adopt winning solutions to forecast the value of automobiles. ACCELERATE YOUR PROGRESS If you want to accelerate your progress and act on what you have learned, this book is the place to get started. It reveals how deep neural networks work, and takes you under the hood with an easy to follow process showing you how to build them faster than you imagined possible using the powerful and free R programming language. Everything you need to get started is contained within this book. Deep Learning for Business With R is your very own hands on practical, tactical, easy to follow guide to mastery Buy this book today your next big breakthrough using deep neural networks is only a page away!
Deep Learning With R For Beginners
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Author : Mark Hodnett
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-20
Deep Learning With R For Beginners written by Mark Hodnett and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-20 with Computers categories.
Explore the world of neural networks by building powerful deep learning models using the R ecosystem Key FeaturesGet to grips with the fundamentals of deep learning and neural networksUse R 3.5 and its libraries and APIs to build deep learning models for computer vision and text processingImplement effective deep learning systems in R with the help of end-to-end projectsBook Description Deep learning finds 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 book 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. This Learning Path includes content from the following Packt products: R Deep Learning Essentials - Second Edition by Joshua F. Wiley and Mark HodnettR Deep Learning Projects by Yuxi (Hayden) Liu and Pablo MaldonadoWhat you will learnImplement credit card fraud detection with autoencodersTrain neural networks to perform handwritten digit recognition using MXNetReconstruct images using variational autoencodersExplore the applications of autoencoder neural networks in clustering and dimensionality reductionCreate natural language processing (NLP) models using Keras and TensorFlow in RPrevent models from overfitting the data to improve generalizabilityBuild shallow neural network prediction modelsWho 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.
Combining Machine Learning And Business
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Author : Yusep Maulana
language : en
Publisher: OYUSEP
Release Date : 2024-04-09
Combining Machine Learning And Business written by Yusep Maulana and has been published by OYUSEP this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-09 with Computers categories.
In today's rapidly advancing digital era, the application of machine learning in various business aspects has become a crucial key in driving innovation and success. "Combining Machine Learning and Business" is designed to provide deep insights into how the integration of machine learning and business strategy can bring about significant transformation. This book not only elaborates on concepts and theories but further takes the reader on an applied research journey that has been undertaken. The author of this book, Yusep Maulana, has spent months conducting in-depth research on the application of machine learning technology in business, with a particular focus on case studies in Switzerland and the Netherlands. Through hands-on experience and direct collaboration with business practitioners in both countries, Yusep has managed to gather valuable data and insights on how machine learning can be integrated into business processes to enhance efficiency, innovation, and competitive advantage. This book presents the results of that research in an accessible way to readers, whether they are academics, business practitioners, students, or anyone with an interest in the field of machine learning and its application in the business world. It is hoped that readers will gain a broader and more applied understanding of the potential and challenges in combining machine learning with business strategy.
Machine Learning With R
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Author : Brett Lantz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-05-29
Machine Learning With R written by Brett Lantz and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-29 with Computers categories.
Learn how to solve real-world data problems using machine learning and R Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features The 10th Anniversary Edition of the bestselling R machine learning book, updated with 50% new content for R 4.0.0 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with this clear, hands-on guide by machine learning expert Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Fourth Edition, provides a hands-on, accessible, and readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to know for data pre-processing, uncovering key insights, making new predictions, and visualizing your findings. This 10th Anniversary Edition features several new chapters that reflect the progress of machine learning in the last few years and help you build your data science skills and tackle more challenging problems, including making successful machine learning models and advanced data preparation, building better learners, and making use of big data. You'll also find this classic R data science book updated to R 4.0.0 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Whether you're looking to take your first steps with R for machine learning or making sure your skills and knowledge are up to date, this is an unmissable read that will help you find powerful new insights in your data. What you will learn Learn the end-to-end process of machine learning from raw data to implementation Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks Prepare, transform, and clean data using the tidyverse Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, Hadoop, H2O, and TensorFlow Who this book is for This book is designed to help data scientists, actuaries, data analysts, financial analysts, social scientists, business and machine learning students, and any other practitioners who want a clear, accessible guide to machine learning with R. No R experience is required, although prior exposure to statistics and programming is helpful.
Deep Learning With R Cookbook
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Author : Swarna Gupta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-02-21
Deep Learning With R Cookbook written by Swarna Gupta and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-21 with Computers categories.
Tackle the complex challenges faced while building end-to-end deep learning models using modern R libraries Key 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 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 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 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.
Mastering Machine Learning With R
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Author : Cory Lesmeister
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-04-24
Mastering Machine Learning With R written by Cory Lesmeister and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-04-24 with Computers categories.
Master machine learning techniques with R to deliver insights in complex projects About This Book Understand and apply machine learning methods using an extensive set of R packages such as XGBOOST Understand the benefits and potential pitfalls of using machine learning methods such as Multi-Class Classification and Unsupervised Learning Implement advanced concepts in machine learning with this example-rich guide Who This Book Is For This book is for data science professionals, data analysts, or anyone with a working knowledge of machine learning, with R who now want to take their skills to the next level and become an expert in the field. What You Will Learn Gain deep insights into the application of machine learning tools in the industry Manipulate data in R efficiently to prepare it for analysis Master the skill of recognizing techniques for effective visualization of data Understand why and how to create test and training data sets for analysis Master fundamental learning methods such as linear and logistic regression Comprehend advanced learning methods such as support vector machines Learn how to use R in a cloud service such as Amazon In Detail This book will teach you advanced techniques in machine learning with the latest code in R 3.3.2. You will delve into statistical learning theory and supervised learning; design efficient algorithms; learn about creating Recommendation Engines; use multi-class classification and deep learning; and more. You will explore, in depth, topics such as data mining, classification, clustering, regression, predictive modeling, anomaly detection, boosted trees with XGBOOST, and more. More than just knowing the outcome, you'll understand how these concepts work and what they do. With a slow learning curve on topics such as neural networks, you will explore deep learning, and more. By the end of this book, you will be able to perform machine learning with R in the cloud using AWS in various scenarios with different datasets. Style and approach The book delivers practical and real-world solutions to problems and a variety of tasks such as complex recommendation systems. By the end of this book, you will have gained expertise in performing R machine learning and will be able to build complex machine learning projects using R and its packages.
R Unleash Machine Learning Techniques
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Author : Raghav Bali
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-10-24
R Unleash Machine Learning Techniques written by Raghav Bali and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-24 with Computers categories.
Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail R is the established language of data analysts and statisticians around the world. And you shouldn't be afraid to use it... This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R. In the first module you'll get to grips with the fundamentals of R. This means you'll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems. For the following two modules we'll begin to investigate machine learning algorithms in more detail. To build upon the basics, you'll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they're all focused on solving real problems in different areas, ranging from finance to social media. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning.
R Machine Learning Essentials
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Author : Michele Usuelli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2014-11-28
R Machine Learning Essentials written by Michele Usuelli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-11-28 with Computers categories.
If you want to learn how to develop effective machine learning solutions to your business problems in R, this book is for you. It would be helpful to have a bit of familiarity with basic object-oriented programming concepts, but no prior experience is required.
Machine Learning For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2023-03-22
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-22 with Computers categories.
MACHINE LEARNING FOR BUSINESS ANALYTICS Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second R edition of Machine Learning for Business Analytics. This edition also includes: A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R An expanded chapter focused on discussion of deep learning techniques A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
Integrating Ai And Machine Learning Into Business And Management Education
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Author : R., Manjunath B.
language : en
Publisher: IGI Global
Release Date : 2025-07-30
Integrating Ai And Machine Learning Into Business And Management Education written by R., Manjunath B. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-30 with Business & Economics categories.
The integration of AI and machine Learning into business education has developed a more efficient way of training future leaders. These technologies enhance the curriculum design and equip students with critical and analytical decision skills for today’s fast paced business environment. AI and machine learning tools foster experiential learning and bridge the gap between theory and practice. As industries increasingly rely on automation and predictive analytics, embedding these technologies into business education is not just innovative, it is imperative for preparing agile, tech-savvy professionals capable of navigating complex organizational challenges. Integrating AI and Machine Learning into Business and Management Education explores how the transformative capabilities of AI and machine learning can be effectively integrated into management education. This book encourages the adoption of AI for personalized learning and advanced problem-solving in management training. Covering topics such as education, AI, and management, this book is an excellent resource for academicians, researchers, corporate trainers, graduates, and policymakers.