R Deep Learning Projects
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R Machine Learning Projects
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Author : Dr. Sunil Kumar Chinnamgari
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-14
R Machine Learning Projects written by Dr. Sunil Kumar Chinnamgari 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 2019-01-14 with Mathematics categories.
Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more Key FeaturesMaster machine learning, deep learning, and predictive modeling concepts in R 3.5Build intelligent end-to-end projects for finance, retail, social media, and a variety of domainsImplement smart cognitive models with helpful tips and best practicesBook Description R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization. This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you’ll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You’ll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine. By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations. What you will learnExplore deep neural networks and various frameworks that can be used in RDevelop a joke recommendation engine to recommend jokes that match users’ tastesCreate powerful ML models with ensembles to predict employee attritionBuild autoencoders for credit card fraud detectionWork with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learningImplement NLP techniques for sentiment analysis and customer segmentationWho this book is for If you’re a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.
R Deep Learning Projects
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing
Release Date : 2018-02-22
R Deep Learning Projects written by Yuxi (Hayden) Liu and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-02-22 with Computers categories.
5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Book Description R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R--including convolutional neural networks, recurrent neural networks, and LSTMs--and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages--such as MXNetR, H2O, deepnet, and more--to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting. What you will learn - Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec - Apply neural networks to perform handwritten digit recognition using MXNet - Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders -Master reconstructing images using variational autoencoders - Wade through sentiment analysis from movie reviews - Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks - Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction Who this book is for Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.
R Deep Learning Projects
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-02-22
R Deep Learning Projects written by Yuxi (Hayden) Liu 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 2018-02-22 with Mathematics categories.
5 real-world projects to help you master deep learning concepts Key Features Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Book Description R is a popular programming language used by statisticians and mathematicians for statistical analysis, and is popularly used for deep learning. Deep Learning, as we all know, is one of the trending topics today, and is finding practical applications in a lot of domains. This book demonstrates end-to-end implementations of five real-world projects on popular topics in deep learning such as handwritten digit recognition, traffic light detection, fraud detection, text generation, and sentiment analysis. You'll learn how to train effective neural networks in R—including convolutional neural networks, recurrent neural networks, and LSTMs—and apply them in practical scenarios. The book also highlights how neural networks can be trained using GPU capabilities. You will use popular R libraries and packages—such as MXNetR, H2O, deepnet, and more—to implement the projects. By the end of this book, you will have a better understanding of deep learning concepts and techniques and how to use them in a practical setting. What you will learn Instrument Deep Learning models with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Apply neural networks to perform handwritten digit recognition using MXNet Get the knack of CNN models, Neural Network API, Keras, and TensorFlow for traffic sign classification -Implement credit card fraud detection with Autoencoders Master reconstructing images using variational autoencoders Wade through sentiment analysis from movie reviews Run from past to future and vice versa with bidirectional Long Short-Term Memory (LSTM) networks Understand the applications of Autoencoder Neural Networks in clustering and dimensionality reduction Who this book is for Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. A knowledge of R programming and the basic concepts of deep learning is required to get the best out of this book.
Deep Learning With R
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Author : Abhijit Ghatak
language : en
Publisher: Springer
Release Date : 2019-04-13
Deep Learning With R written by Abhijit Ghatak and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-13 with Computers categories.
Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning. The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks.
Hands On Machine Learning With R
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Author : Brad Boehmke
language : en
Publisher: CRC Press
Release Date : 2019-11-07
Hands On Machine Learning With R written by Brad Boehmke and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-07 with Business & Economics categories.
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Python Deep Learning Projects
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Author : Matthew Lamons
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31
Python Deep Learning Projects written by Matthew Lamons 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 2018-10-31 with Computers categories.
Insightful projects to master deep learning and neural network architectures using Python and Keras Key FeaturesExplore deep learning across computer vision, natural language processing (NLP), and image processingDiscover best practices for the training of deep neural networks and their deploymentAccess popular deep learning models as well as widely used neural network architecturesBook Description Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier. Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system. Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects. By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way What you will learnSet up a deep learning development environment on Amazon Web Services (AWS)Apply GPU-powered instances as well as the deep learning AMIImplement seq-to-seq networks for modeling natural language processing (NLP)Develop an end-to-end speech recognition systemBuild a system for pixel-wise semantic labeling of an imageCreate a system that generates images and their regionsWho this book is for Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming
Deep Learning With R
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Author : François Chollet
language : en
Publisher: Simon and Schuster
Release Date : 2018-01-22
Deep Learning With R written by François 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 2018-01-22 with Computers categories.
Summary Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-learning-with-r-in-motion). Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks. About the Book Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You'll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image classification and generation Deep learning for text and sequences About the Reader You'll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed. About the Authors François Chollet is a deep-learning researcher at Google and the author of the Keras library. J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions
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.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Computers categories.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Deep Learning For Coders With Fastai And Pytorch
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Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala