Introduction To Deep Learning Using R
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Introduction To Deep Learning Using R
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Author : Taweh Beysolow II
language : en
Publisher: Apress
Release Date : 2017-07-19
Introduction To Deep Learning Using R written by Taweh Beysolow II and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-19 with Computers categories.
Understand deep learning, the nuances of its different models, and where these models can be applied. The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. What You'll Learn Understand the intuition and mathematics that power deep learning models Utilize various algorithms using the R programming language and its packages Use best practices for experimental design and variable selection Practice the methodology to approach and effectively solve problems as a data scientist Evaluate the effectiveness of algorithmic solutions and enhance their predictive power Who This Book Is For Students, researchers, and data scientists who are familiar with programming using R. This book also is also of use for those who wish to learn how to appropriately deploy these algorithms in applications where they would be most useful.
Deep Learning With R Second Edition
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Author : Francois Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2022-09-13
Deep Learning With R Second Edition written by Francois Chollet and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-13 with Computers categories.
Deep learning from the ground up using R and the powerful Keras library! In Deep Learning with R, Second Edition you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. About the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. About the book Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library. What's inside Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation About the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required. About the author François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for time series 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions
Deep Learning Architectures
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Author : Ovidiu Calin
language : en
Publisher: Springer Nature
Release Date : 2020-02-13
Deep Learning Architectures written by Ovidiu Calin and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-13 with Mathematics categories.
This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.
R Deep Learning Essentials
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Author : Mark Hodnett
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-24
R Deep Learning Essentials 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 2018-08-24 with Computers categories.
Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for computer vision and text Apply deep learning techniques in cloud for large-scale processing Build, train, and optimize neural network models on a range of datasets Book Description Deep learning is a powerful subset of machine learning that is very successful in domains such as computer vision and natural language processing (NLP). This second edition of R Deep Learning Essentials will open the gates for you to enter the world of neural networks by building powerful deep learning models using the R ecosystem. This book will introduce you to the basic principles of deep learning and teach you to build a neural network model from scratch. As you make your way through the book, you will explore deep learning libraries, such as Keras, MXNet, and TensorFlow, and create interesting deep learning models for a variety of tasks and problems, including structured data, computer vision, text data, anomaly detection, and recommendation systems. You’ll cover advanced topics, such as generative adversarial networks (GANs), transfer learning, and large-scale deep learning in the cloud. In the concluding chapters, you will learn about the theoretical concepts of deep learning projects, such as model optimization, overfitting, and data augmentation, together with other advanced topics. By the end of this book, you will be fully prepared and able to implement deep learning concepts in your research work or projects. What you will learn Build shallow neural network prediction models Prevent models from overfitting the data to improve generalizability Explore techniques for finding the best hyperparameters for deep learning models Create NLP models using Keras and TensorFlow in R Use deep learning for computer vision tasks Implement deep learning tasks, such as NLP, recommendation systems, and autoencoders Who this book is for This second edition of R Deep Learning Essentials 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. Fundamental understanding of the R language is necessary to get the most out of this book.
Multivariate Statistical Machine Learning Methods For Genomic Prediction
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Author : Osval Antonio Montesinos López
language : en
Publisher: Springer Nature
Release Date : 2022-02-14
Multivariate Statistical Machine Learning Methods For Genomic Prediction written by Osval Antonio Montesinos López and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Technology & Engineering categories.
This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Mathematical Engineering Of Deep Learning
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Author : Benoit Liquet
language : en
Publisher: CRC Press
Release Date : 2024-10-03
Mathematical Engineering Of Deep Learning written by Benoit Liquet and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-03 with Computers categories.
Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning. Key Features: A perfect summary of deep learning not tied to any computer language, or computational framework. An ideal handbook of deep learning for readers that feel comfortable with mathematical notation. An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains. The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials. Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.
Optimization Machine Learning And Fuzzy Logic Theory Algorithms And Applications
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Author : Mzili, Toufik
language : en
Publisher: IGI Global
Release Date : 2025-02-20
Optimization Machine Learning And Fuzzy Logic Theory Algorithms And Applications written by Mzili, Toufik and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
Optimization, machine learning, and fuzzy logic are fundamental in the field of computational intelligence, each contributing to solving complex problems across various domains. Optimization techniques focus on finding the best solutions to problems by improving efficiency and minimizing resources. Machine learning enables systems to learn from data, making predictions or decisions without being programmed. Fuzzy logic deals with uncertainty and imprecision, allowing for flexible decision-making processes. Together, these theories, algorithms, and applications solve challenges in fields such as engineering, finance, and healthcare, where traditional methods often fall short. The continued application and exploration of these disciplines may unveil new possibilities for advanced problem-solving and intelligent systems. Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications explores optimization techniques, fuzzy logic, and their integration with machine learning. It covers fundamental concepts, mathematical foundations, algorithms, and applications, providing a holistic understanding of these domains. This book covers topics such as disease detection, deep learning, and text analysis, and is a useful resource for engineers, data scientists, medical professionals, academicians, and researchers.
Deep Learning And Iot In Healthcare Systems
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Author : Krishna Kant Singh
language : en
Publisher: CRC Press
Release Date : 2021-12-15
Deep Learning And Iot In Healthcare Systems written by Krishna Kant Singh and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-15 with Computers categories.
This new volume discusses the applications and challenges of deep learning and the internet of things for applications in healthcare. It describes deep learning techniques in conjunction with IoT used by practitioners and researchers worldwide. The authors explore the convergence of IoT and deep learning to enable things to communicate, share information, and coordinate decisions. The book includes deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. Chapters look at assistive devices in healthcare, alerting and detection devices, energy efficiency in using IoT, data mining for gathering health information for individuals with autism, IoT for mobile applications, and more. The text also offers mathematical and conceptual background that presents the latest technology as well as a selection of case studies.
Deep Learning Neural Network And Beyond
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Author : Dr. S. Suganya
language : en
Publisher: Xoffencer international book publication house
Release Date : 2024-07-05
Deep Learning Neural Network And Beyond written by Dr. S. Suganya and has been published by Xoffencer international book publication house this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-05 with Computers categories.
Deep learning has brought about a revolution in the field of artificial intelligence by providing sophisticated tools that can be used to solve difficult issues in a variety of fields. One of the most important components of deep learning is the neural network, which is a computational model that is modeled after the structure and function of the human brain. Neural networks are made up of neurons, which are nodes that are connected to one another and are arranged in layers. Input data is processed by each neuron, and signals are then transmitted to neurons in the subsequent layer, which finally results in the production of output. The process of neural networks learning from data is referred to as backpropagation. This involves altering the strength of connections between neurons in order to reduce the amount of errors that occur in their predictions. However, the scope of deep learning encompasses a much wider range of applications than typical neural networks. In order to improve the capabilities of these models, researchers are continually investigating novel structures and methods. Examples of neural networks that are specifically developed for processing grid-like data include convolutional neural networks (CNNs), which are used to process images. Convolutional neural networks (CNNs) are able to effectively capture spatial hierarchies in visual input by utilizing convolutional layers. This enables CNNs to perform tasks such as image categorization and object detection with exceptional accuracy. The use of recurrent neural networks (RNNs) is another key innovation that is particularly well-suited for sequential data processing tasks. Some examples of these tasks include the understanding of natural language and the prediction of time series. In contrast to feedforward neural networks, recurrent neural networks (RNNs) feature connections that create directed cycles, which provide them with the ability to remember previous inputs. The ability of RNNs to record temporal connections in data is made possible by this memory, which makes them extremely useful for jobs that require context or continuity. In addition to these well-established designs, academics are investigating more unusual models such as transformers and generative adversarial networks (GANs). An artificial neural network (GAN) is made up of two neural networks—a generator and a discriminator—that are involved in a process of competitive learning. Because of this configuration, GANs are able to generate synthetic data that is realistic, which has a wide range of applications, including drug discovery and image synthesis.
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!