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Soft cover. Condizione: Near Fine. 1st Edition. A detailed study of the use of data science to enhance fundraising operations. 522 pages. Has a previous owner embossed stamp on the title page, otherwise no sign of previous use.

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Condizione: New. Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R (Paperback or Softback).

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Condizione: New. pp. 330.

Paperback. Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Paperback. Condizione: New.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Print on Demand pp. 568.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Print on Demand pp. 568.

Condizione: New. PRINT ON DEMAND pp. 568.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Print on Demand pp. 330.

Condizione: New. PRINT ON DEMAND pp. 330.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and DeepnetKey Features Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem Improve models using parameter tuning, feature engineering, and ensembling Apply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domainsBook DescriptionDeep 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 learn Design a feedforward neural network to see how the activation function computes an output Create an image recognition model using convolutional neural networks (CNNs) Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm Apply text cleaning techniques to remove uninformative text using NLP Build, train, and evaluate a GAN model for face generation Understand the concept and implementation of reinforcement learning in RWho this book is forThis 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.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Deep learning enables efficient and accurate learning from data. Developers working with R will be able to put their knowledge to work with this practical guide to deep learning. The book provides a hands-on approach to implementation and associated methodo.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Discover the techniques used by the top R programmers to generate data-driven solutions.Although the non-profit industry has advanced using CRMs and donor databases, it has not fully explored the data stored in those databases. Meanwhile, the data scientists, in the for-profit industry, using sophisticated tools, have generated data-driven results and effective solutions for several challenges in their organizations.Wouldn't you like to learn these data science techniques to solve fundraising problems After reading Data Science for Fundraising, you can:¿ Begin your data science journey with R¿ Import data from Excel, text and CSV files, and databases, such as sqllite and Microsoft's SQL Server¿ Apply data cleanup techniques to remove unnecessary characters and whitespace¿ Manipulate data by removing, renaming, and ordering rows and columns¿ Join data frames using dplyr¿ Perform Exploratory Data Analysis by creating box-plots, histograms, and Q-Q plots¿ Understand effective data visualization principles, best practices, and techniques¿ Use the right chart type after understanding the advantages and disadvantages of different chart types¿ Create beautiful maps by ZIP code, county, and state¿ Overlay maps with your own data¿ Create elegant data visualizations, such as heat maps, slopegraphs, and animated charts¿ Become a data visualization expert¿ Create Recency, Frequency, Monetary (RFM) models¿ Build predictive models using machine learning techniques, such as K-nearest neighbor, Naive Bayes, decision trees, random forests, gradient boosting, and neural network¿ Build deep learning neural network models using TensorFlow¿ Predict next transaction amount using regression and machine learning techniques, such as neural networks and quantile regression¿ Segment prospects using clustering and association rule mining¿ Scrape data off the web and create beautiful reports from that data¿ Predict sentiment using text mining and Twitter data¿ Analyze social network data using measures, such as betweenness, centrality, and degrees¿ Visualize social networks by building beautiful static and interactive maps¿ Learn the industry-transforming trendsRegardless of your skill level, you can equip yourself and help your organization succeed with these data science techniques using R.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &Uumlber den AutorrnrnThe author of Tableau Data Visualization Cookbook, Ashutosh R. Nandeshwar is one of the few analytics professionals in the higher education industry who has developed analytical solutions for all stages of the student life.…