Deep Learning With R For Beginners
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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.
Data Science For Beginners
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Author : Russel R Russo
language : en
Publisher:
Release Date : 2020-12-17
Data Science For Beginners written by Russel R Russo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-17 with categories.
Are you fascinated by Data Science but it seems too complicated? Do you want to learn everything about Artificial Intelligence but it looks like it is an exclusive club? If this is you, please keep reading: you are in the right place, looking at the right book. SInce you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Everyone knows (well, almost everyone) how important Data Science is for the growth and success of the biggest tech companies, and many people know about the Machine Learning impact in science, medicine and statistics. Also, it is quite commonly known that Artificial Intelligence, Machine Learning Deep Learning, and the mastering of their most important language, Python, can offer a lot of possibilities in work and business. And you yourself are probably thinking "I surely can see that opportunity, but how can I seize it?" Well, if you kept reading so far you are on the right track to answer your question. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Python and Neural Networks programming is The Skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to find your way around Artificial Intelligence, Data Science, Deep Learning and Machine Learning, and to do that Data Science for Beginners is the best way. In Data Science for Beginners you will discover: The most effective starting points when training deep neural nets The smartest way to approach Machine Learning What libraries are and which one is the best for you Tips and tricks for a smooth and painless journey into artificial intelligence Why decision tree is the way The TensorFlow parts that are going to make your coding life easy Why python is the best language for Machine Learning How to bring your ideas into a computer How to talk with deep neural networks How to deal with variables and data The most common myths about Machine Learning debunked Even If you don't know anything about programming, understanding Data Science is the ideal place to start. Still, if you already know something about programming but not about how to apply it to Artificial Intelligence, Data Science is what you want to understand. Buy now Data Science for Beginners to start your path of Artificial Intelligence.
Neural Networks For Beginners
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Author : Russel R Russo
language : en
Publisher:
Release Date : 2019-11-06
Neural Networks For Beginners written by Russel R Russo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-06 with categories.
Do you want to understand Neural Networks and learn everything about them but it looks like it is an exclusive club? Are you fascinated by Artificial Intelligence but you think that it would be too difficult for you to learn? If you think that Neural Networks and Artificial Intelligence are the present and, even more, the future of technology, and you want to be part of it... well you are in the right place, and you are looking at the right book. If you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Neural Networks programming is the skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to program and use neural networks, and to do that Neural Networks for Beginners is the perfect way. In this book you will learn: The types and components of neural networks The smartest way to approach neural network programming Why Algorithms are your friends The "three Vs" of Big Data (plus two new Vs) How machine learning will help you making predictions The three most common problems with Neural Networks and how to overcome them Even if you don't know anything about programming, Neural Networks is the perfect place to start now. Still, if you already know about programming but not about how to do it in Artificial Intelligence, neural networks are the next thing you want to learn. And Neural Networks for Beginners is the best way to do it. Download Neural Network for Beginners now to get the best start for your journey to Artificial Intelligence. Scroll to the top of the page and click the BUY NOW button.
Machine Learning With R Quick Start Guide
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Author : Iván Pastor Sanz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-29
Machine Learning With R Quick Start Guide written by Iván Pastor Sanz 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 2019-03-29 with Computers categories.
Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key FeaturesUse R 3.5 to implement real-world examples in machine learningImplement key machine learning algorithms to understand the working mechanism of smart modelsCreate end-to-end machine learning pipelines using modern libraries from the R ecosystemBook Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learnIntroduce yourself to the basics of machine learning with R 3.5Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your resultsLearn to build predictive models with the help of various machine learning techniquesUse R to visualize data spread across multiple dimensions and extract useful featuresUse interactive data analysis with R to get insights into dataImplement supervised and unsupervised learning, and NLP using R librariesWho this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.
Machine Learning With R
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Author : Dominic Lordy
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-05-27
Machine Learning With R written by Dominic Lordy and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-05-27 with categories.
***** BUY NOW (Will soon return to 25.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using R? If you are looking for a complete beginners guide to learn Machine Learning using R, in just a few hours, this book is for you. Machine Learning is the practice of transforming data into knowledge, and R is the most popular open-source programming language used for Machine Learning. In this book, we will learn how to use the principles of Machine Learning and the R programming language to answer day-to-day questions about your data. Finally, we'll learn how to make predictions with machine learning. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all important R and Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach Machine Learning, but are too afraid of complex math to start Newbies in computer science techniques and machine learning Professionals in Machine Learning and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on Machine Learning What's Inside This Book? Introduction Basic Functions Linear Regression Machine Learning Algorithms Data with R Generating data Graphical functions Programming with R in Practice Opening the Black Box K-nearest Neighbors Neural Networks Trees and Forests Standard Linear Model Logistic Regression Support Vector Machine using R Frequently Asked Questions Help! I got an error, what did I do wrong? Useful References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: f you want to smash Machine Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected]. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at https: //aisciences.lpages.co/ai-sciences-data-science-with-r/
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.
Machine Learning Made Easy With R
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Author : N. Lewis
language : en
Publisher:
Release Date : 2017-05-07
Machine Learning Made Easy 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 2017-05-07 with categories.
Finally, A Blueprint for Machine Learning with R! Machine Learning Made Easy with R offers a practical tutorial that uses hands-on examples to step through real-world applications using clear and practical case studies. Through this process it takes you on a gentle, fun and unhurried journey to creating machine learning models with R. Whether you are new to data science or a veteran, this book offers a powerful set of tools for quickly and easily gaining insight from your data using R. NO EXPERIENCE REQUIRED: This book uses plain language rather than a ton of equations; 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 try successful machine learning algorithms for yourself. YOUR PERSONAL BLUE PRINT: Through a simple to follow intuitive step by step process, you will learn how to use the most popular machine learning algorithms using R. Once you have mastered the process, it will be easy for you to translate your knowledge to assess your own data. THIS BOOK IS FOR YOU IF YOU WANT: Focus on explanations rather than mathematical derivation Practical illustrations that use real data. 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. TAKE THE SHORTCUT: This guide was written for people just like you. Individuals who want to get up to speed as quickly as possible. to: YOU'LL LEARN HOW TO: Unleash the power of Decision Trees. Develop hands on skills using k-Nearest Neighbors. Design successful applications with Naive Bayes. Deploy Linear Discriminant Analysis. Explore Support Vector Machines. Master Linear and logistic regression. Create solutions with Random Forests. Solve complex problems with Boosting. Gain deep insights via K-Means clustering. Acquire tips to enhance model performance. For each machine learning algorithm, every step in the process is detailed, from preparing the data for analysis, to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks. Using plain language, this book offers a simple, intuitive, practical, non-mathematical, easy to follow guide to the most successful ideas, outstanding techniques and usable solutions available using R. Everything you need to get started is contained within this book. Machine Learning Made Easy with R is your very own hands on practical, tactical, easy to follow guide to mastery. Buy this book today and accelerate your progress!
Beginning R
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Author : Mark Gardener
language : en
Publisher: John Wiley & Sons
Release Date : 2012-06-05
Beginning R written by Mark Gardener 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 2012-06-05 with Computers categories.
Gain better insight into your data using the power of R While R is very flexible and powerful, it is unlike most of the computer programs you have used. In order to unlock its full potential, this book delves into the language, making it accessible so you can tackle even the most complex of data analysis tasks. Simple data examples are integrated throughout so you can explore the capabilities and versatility of R. Along the way, you'll also learn how to carry out a range of commonly used statistical methods, including Analysis of Variance and Linear Regression. By the end, you'll be able to effectively and efficiently analyze your data and present the results. Beginning R: Discusses how to implement some basic statistical methods such as the t-test, correlation, and tests of association Explains how to turn your graphs from merely adequate to simply stunning Provides you with the ability to define complex analytical situations Demonstrates ways to make and rearrange your data for easier analysis Covers how to carry out basic regression as well as complex model building and curvilinear regression Shows how to produce customized functions and simple scripts that can automate your workflow wrox.com Programmer Forums Join our Programmer to Programmer forums to ask and answer programming questions about this book, join discussions on the hottest topics in the industry, and connect with fellow programmers from around the world. Code Downloads Take advantage of free code samples from this book, as well as code samples from hundreds of other books, all ready to use. Read More Find articles, ebooks, sample chapters and tables of contents for hundreds of books, and more reference resources on programming topics that matter to you. Wrox Beginning guides are crafted to make learning programming languages and technologies easier than you think, providing a structured, tutorial format that guides you through all the techniques involved. Visit the Beginning R website at www.wrox.com/go/beginningr
Hands On Deep Learning With R
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Author : Michael Pawlus
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-04-24
Hands On Deep Learning With R written by Michael Pawlus 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 2020-04-24 with Computers categories.
Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and Deepnet Key FeaturesUnderstand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problemImprove models using parameter tuning, feature engineering, and ensemblingApply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domainsBook Description Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms. What you will learnDesign a feedforward neural network to see how the activation function computes an outputCreate an image recognition model using convolutional neural networks (CNNs)Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithmApply text cleaning techniques to remove uninformative text using NLPBuild, train, and evaluate a GAN model for face generationUnderstand the concept and implementation of reinforcement learning in RWho this book is for This book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.
Advanced Deep Learning With R
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Author : Bharatendra Rai
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-17
Advanced Deep Learning With R written by Bharatendra Rai 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 2019-12-17 with Computers categories.
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key 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 Description Deep 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 for This 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.