Panduan Praktis Membangun Crud Back End Dengan Golang
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PANDUAN PRAKTIS MEMBANGUN CRUD BACK-END DENGAN GOLANG
Author: Muammar Alfien Zaidan
language: id
Publisher: Penerbit Buku Pedia
Release Date: 2023-08-02
Buku ini berisi tentang tutorial atau langkah-langkah membuat aplikasi Go Lang yang memiliki fitur CRUD (Create, Read, Update, Delete) menggunakan bahasa pemrograman Go sebagai web service. Aplikasi yang dihasilkan akan berupa aplikasi back-end yang dapat dijalankan sebagai server untuk melakukan operasi CRUD dan dapat diuji coba menggunakan aplikasi client apapun seperti Postman, dan aplikasi client lainnya yang dapat mengakses API dari aplikasi yang dibangun.
Biomedical Signal Processing
This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing.
Text Mining with R
Author: Julia Silge
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-06-12
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as data frames enables you to manipulate, summarize, and visualize characteristics of text. You’ll also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data explorations will help you generate real insights from literature, news, and social media. Learn how to apply the tidy text format to NLP Use sentiment analysis to mine the emotional content of text Identify a document’s most important terms with frequency measurements Explore relationships and connections between words with the ggraph and widyr packages Convert back and forth between R’s tidy and non-tidy text formats Use topic modeling to classify document collections into natural groups Examine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages