Deep Learning And Scientific Computing With R Torch
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Deep Learning And Scientific Computing With R Torch
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Author : Sigrid Keydana
language : en
Publisher: CRC Press
Release Date : 2023-04-06
Deep Learning And Scientific Computing With R Torch written by Sigrid Keydana and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-06 with Business & Economics categories.
torch is an R port of PyTorch, one of the two most-employed deep learning frameworks in industry and research. It is also an excellent tool to use in scientific computations. It is written entirely in R and C/C++. Though still "young" as a project, R torch already has a vibrant community of users and developers. Experience shows that torch users come from a broad range of different backgrounds. This book aims to be useful to (almost) everyone. Globally speaking, its purposes are threefold: - Provide a thorough introduction to torch basics – both by carefully explaining underlying concepts and ideas, and showing enough examples for the reader to become "fluent" in torch. - Again with a focus on conceptual explanation, show how to use torch in deep-learning applications, ranging from image recognition over time series prediction to audio classification. - Provide a concepts-first, reader-friendly introduction to selected scientific-computation topics (namely, matrix computations, the Discrete Fourier Transform, and wavelets), all accompanied by torch code you can play with. Deep Learning and Scientific Computing with R torch is written with first-hand technical expertise and in an engaging, fun-to-read way.
A Greater Foundation For Machine Learning Engineering
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Author : Dr. Ganapathi Pulipaka
language : en
Publisher: Xlibris Corporation
Release Date : 2021-10-01
A Greater Foundation For Machine Learning Engineering written by Dr. Ganapathi Pulipaka and has been published by Xlibris Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-01 with Computers categories.
This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.
Deep Learning With Tensorflow
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Author : Giancarlo Zaccone
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-03-30
Deep Learning With Tensorflow written by Giancarlo Zaccone 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 2018-03-30 with Computers categories.
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key Features Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Gain real-world contextualization through some deep learning problems concerning research and application Book Description Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. What you will learn Apply deep machine intelligence and GPU computing with TensorFlow Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications Who this book is for The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.
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.
Spatial Data Science
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Author : Edzer Pebesma
language : en
Publisher: CRC Press
Release Date : 2023-05-10
Spatial Data Science written by Edzer Pebesma and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-10 with Mathematics categories.
-Written by the authors of key spatial R packages -Makes spatial data analysis more robust -Integrates with the tidyverse and comparable approaches -Includes many easily reproducible examples
Learn R
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Author : Pedro J. Aphalo
language : en
Publisher: CRC Press
Release Date : 2024-04-26
Learn R written by Pedro J. Aphalo and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-26 with Computers categories.
Learning a computer language like R can be either frustrating, fun or boring. Having fun requires challenges that wake up the learner’s curiosity but also provide an emotional reward for overcoming them. The book is designed so that it includes smaller and bigger challenges, in what I call playgrounds, in the hope that all readers will enjoy their path to R fluency. Fluency in the use of a language is a skill that is acquired through practice and exploration. For students and professionals in the biological sciences, humanities and many applied fields, recognizing the parallels between R and natural languages should help them feel at home with R. The approach I use is similar to that of a travel guide, encouraging exploration and describing the available alternatives and how to reach them. The intention is to guide the reader through the R landscape of 2024 and beyond. What is new in the second edition? Text expanded by more than 25% to include additional R features and gentler and more detailed explanations Contains 24 new diagrams and flowcharts, seven new tables, and revised text and code examples for clarity All three indexes were expanded, and answers to 28 frequently asked questions added What will you find in this book? Programming concepts explained as they apply to current R Emphasis on the role of abstractions in programming Few prescriptive rules—mostly the author’s preferences together with alternatives Presentation of the R language emphasizing the “R way of doing things” Tutoring for “programming in the small” using scripts for data analysis Explanation of the differences between R proper and extensions for data wrangling The grammar of graphics is described as a language for the construction of data visualisations Examples of data exchange between R and the foreign world using common file formats Coaching to become an independent R user, capable of writing original scripts and solving future challenges
R Deep Learning Cookbook
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Author : Dr. PKS Prakash
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-08-04
R Deep Learning Cookbook written by Dr. PKS Prakash 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 2017-08-04 with Computers categories.
Powerful, independent recipes to build deep learning models in different application areas using R libraries About This Book Master intricacies of R deep learning packages such as mxnet & tensorflow Learn application on deep learning in different domains using practical examples from text, image and speech Guide to set-up deep learning models using CPU and GPU Who This Book Is For Data science professionals or analysts who have performed machine learning tasks and now want to explore deep learning and want a quick reference that could address the pain points while implementing deep learning. Those who wish to have an edge over other deep learning professionals will find this book quite useful. What You Will Learn Build deep learning models in different application areas using TensorFlow, H2O, and MXnet. Analyzing a Deep boltzmann machine Setting up and Analysing Deep belief networks Building supervised model using various machine learning algorithms Set up variants of basic convolution function Represent data using Autoencoders. Explore generative models available in Deep Learning. Discover sequence modeling using Recurrent nets Learn fundamentals of Reinforcement Leaning Learn the steps involved in applying Deep Learning in text mining Explore application of deep learning in signal processing Utilize Transfer learning for utilizing pre-trained model Train a deep learning model on a GPU In Detail Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems. Style and approach Collection of hands-on recipes that would act as your all-time reference for your deep learning needs
Tidy Finance With R
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Author : Christoph Scheuch
language : en
Publisher: CRC Press
Release Date : 2023-04-05
Tidy Finance With R written by Christoph Scheuch and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-05 with Business & Economics categories.
This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages. We then provide the code to prepare common open source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques. Highlights 1. Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance. 2. Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copy-pasting the code we provide. 3. A full-fledged introduction to machine learning with tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. 4. Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets in the field of financial economics: CRSP and Compustat. The chapter also contains detailed explanations of the most relevant data characteristics. 5. Each chapter provides exercises that are based on established lectures and exercise classes and which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises.
Model Based Clustering Classification And Density Estimation Using Mclust In R
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Author : Luca Scrucca
language : en
Publisher: CRC Press
Release Date : 2023-04-20
Model Based Clustering Classification And Density Estimation Using Mclust In R written by Luca Scrucca and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-20 with Mathematics categories.
Model-Based Clustering, Classification, and Denisty Estimation Using mclust in R Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models. Key features of the book: An introduction to the model-based approach and the mclust R package A detailed description of mclust and the underlying modeling strategies An extensive set of examples, color plots, and figures along with the R code for reproducing them Supported by a companion website, including the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material Model-Based Clustering, Classification, and Density Estimation Using mclust in R is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods, including inference and computing. In addition to serving as a reference manual for mclust, the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics, data science, clinical research, social science, and many other disciplines.
Spatial Analysis In Geology Using R
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Author : Pedro M. Nogueira
language : en
Publisher: CRC Press
Release Date : 2024-07-01
Spatial Analysis In Geology Using R written by Pedro M. Nogueira and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-01 with Mathematics categories.
The integration of geology with data science disciplines, such as spatial statistics, remote sensing, and geographic information systems (GIS), has given rise to a shift in many natural sciences schools, pushing the boundaries of knowledge and enabling new discoveries in geological processes and earth systems. Spatial analysis of geological data can be used to identify patterns and trends in data, to map spatial relationships, and to model spatial processes. R is a consolidated and yet growing statistical programming language with increasing value in spatial analysis often replacing, with advantage, GIS tools. By providing a comprehensive guide for geologists to harness the power of spatial analysis in R, Spatial Analysis in Geology Using R serves as a tool in addressing real-world problems, such as natural resource management, environmental conservation, and hazard prediction and mitigation. Features: Provides a practical and accessible overview of spatial analysis in geology using R Organised in three independent and complementary parts: Introduction to R, Spatial Analysis with R, and Spatial Statistics and Modelling Applied approach with many detailed examples and case studies using real geological data Presents a collection of R packages that are useful in many geological situations Does not assume any prior knowledge of R; all code are explained in detail Supplemented by a website with all data, code, and examples Spatial Analysis in Geology Using R will be useful to any geological researcher who has acquired basic spatial analysis skills, often using GIS, and is interested in deepening those skills through the use of R. It could be used as a reference by applied researchers and analysts in public, private, or third-sector industries. It could also be used to teach a course on the topic to graduate students or for self-study.