Discovery wikipedia的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列股價、配息、目標價等股票新聞資訊

Social Media Data Mining and Analytics

為了解決Discovery wikipedia的問題，作者Szabo, Gabor/ Polatkan, Gungor/ Boykin, Oscar/ Chalkiopoulos, An 這樣論述：

Harness the power of social media to predict customer behavior and improve salesSocial media is the biggest source of Big Data. Because of this, 90% of Fortune 500 companies are investing in Big Data initiatives that will help them predict consumer behavior to produce better sales results. Social Me

dia Data Mining and Analytics shows analysts how to use sophisticated techniques to mine social media data, obtaining the information they need to generate amazing results for their businesses.Social Media Data Mining and Analytics isn't just another book on the business case for social media. Rathe

r, this book provides hands-on examples for applying state-of-the-art tools and technologies to mine social media - examples include Twitter, Wikipedia, Stack Exchange, LiveJournal, movie reviews, and other rich data sources. In it, you will learn: The four key characteristics of online services-use

rs, social networks, actions, and contentThe full data discovery lifecycle-data extraction, storage, analysis, and visualizationHow to work with code and extract data to create solutionsHow to use Big Data to make accurate customer predictionsHow to personalize the social media experience using mach

ine learningUsing the techniques the authors detail will provide organizations the competitive advantage they need to harness the rich data available from social media platforms. GABOR SZABO, PHD, is a Senior Staff Software Engineer at Tesla and a former data scientist at Twitter, where he focuse

d on predicting user behavior and content popularity in crowdsourced online services, and on modeling large-scale content dynamics. He also authored the PyCascading data processing library. GUNGOR POLATKAN, PHD, is a Tech Lead/Engineering Manager designing and implementing end-to-end machine learni

ng and artificial intelligence offline/online pipelines for the LinkedIn Learning relevance backend. He was previously a machine learning scientist at Twitter, where he worked on topics such as ad targeting and user modeling. P. OSCAR BOYKIN, PHD, is a software engineer at Stripe where he works on

machine learning infrastructure. He was previously a Senior Staff Engineer at Twitter, where he worked on data infrastructure problems. He is coauthor of the Scala big-data libraries Algebird, Scalding and Summingbird. ANTONIOS CHALKIOPOULOS, MSC, is a Distributed Systems Specialist. A system engin

eer who has delivered fast/big data projects in media, betting, and finance, he is now leading the effort on the Lenses platform for data streaming as a co-founder and CEO at https: //lenses.stream.

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探索智慧物聯網研究：文獻計量分析與主題建模方法

為了解決Discovery wikipedia的問題，作者吳慶福 這樣論述：

為清楚勾勒出智慧物聯網研究發展樣貌，本研究探索Web of Science 1975年至2021年5,436篇「智慧物聯網」為主題的文獻。經文獻計量分析發現：(1)文獻出版年份為2012-2021年，2012-2016年為生長期，2017-2021年為發展期；(2)《IEEE Internet of Things Journal》是AIoT議題最具影響力的期刊；(3)‪中國大陸、美國、印度發表篇數分居前3名，臺灣位居第9名；(4) AIoT文獻可區分「工業4.0管理、智慧城市治理及未來挑戰」等7個集群。以潛在狄利克雷分佈(Latent Dirichlet allocation, LDA)發現

文獻聚焦在「智慧醫療」等6個主題。綜觀文獻計量分析關鍵字共現聚類，以及LDA潛在主題重點，均關注智慧醫療、工業4.0、資通安全及隱私保護的議題。就AIoT國防應用，提列「智慧物聯網多元軍事應用」等2項建議，並對國軍人事等8個業務工作面向，提供「人才招募客服聊天機器人」等21項AIoT可行方案，藉由導入智慧物聯網，提升智慧國防戰力，帶動全民支持及參與國防。透過上述研究發現，以及文獻計量分析、LDA主題建模的分析過程，可有效探討智慧物聯網研究，迅速掌握領域研究樣貌，並且提供後續相關研究納為參考與指引。

The Enchantress of Numbers: The Story of Ada Lovelace

為了解決Discovery wikipedia的問題，作者Christianson, Jessica 這樣論述：

Imagine a life with no computers; no Google, Facebook, Wikipedia, or Amazon That is the world that Ada Lovelace lived in, alongside Charles Babbage, the inventor of the world's first counting machine. Together the pair would lay the foundation for the world's first computer code, which is still in

use today. Not only did Ada use her skills in calculus, arithmetic, and language to change the world, she changed what it meant to be a woman at the time when she chose a lifechanging career in math. learn about her ground breaking discoveries and inspirational determination in this tale of self-dis

covery and adventure The Enchantress of Numbers is the fourth book in the four part series, STEM Super-heroines, published by Girls Rock Math. GRM aims to inspire youth with stories of the brave, creative, and accomplished women that have helped shape mathematics as we know it today. Girls Rock Mat

h is a program that aims to provide thought provoking, creative experiences in math, empowering girls to develop confidence in their skills and a life-long interest in mathematics. Jessica is a Washington native and loves living in the Pacific Northwest. She lives on Bainbridge Island with her you

ng son, where they love to explore the local playgrounds, beaches, and hiking trails together on sunny days. Jessica, a former elementary school teacher, created the Girls Rock Math program to empower more girls to feel confident in math. Writing these books about inspiring women in the field of mat

hematics is one way she hopes to inspire young girls.

應用深度學習於時空資料預測

為了解決Discovery wikipedia的問題，作者郭有富 這樣論述：

在本論文中，對時空數據挖掘網絡進行了廣泛的探討並使用火災事件數據集對這些網絡模型進行了比較。本文解決兩個問題： 1. 在最近提出的 STDM-DL（時空數據挖掘，深度學習）模型中，比較這些模型的預測能力？ 2. 當應用於火災數據集時，這些模型的性能如何？本論文進行了兩個實驗。第一個是使用他們的數據運行最先進的 STDM-DL 模型並比較它們的性能。本研究下的模型由 METR-LA 或 PEMS-BAY 數據集訓練，預測空間和時間域中的交通。在第二個實驗中，我們使用了新北市的火警數據集 (NTPC-Fire 2015-17) 並實現了一些熟悉但簡單的模型，例如自動編碼器和 GAN，以重建（預測

）光柵化熱圖。然後，我們使用 LSTM-RNN、FBProphet 和 ARIMA 處理時間表示，以比較每日和每週事件頻率的時間序列預測的性能。我們的第一個實驗發現一些最先進的型，例如 ST-MetaNet、STGCN 和 Spacetimeformer，都具有相似的性能。“Deepforecast Multi-LSTM”是迄今為止最好的交通預測模型。令人驚訝的是，在我們的第二個實驗中，對於我們的數據集，FBProphet 模型是我們最好的時間模型，具有 6.97231 RMSE 和 5.045342 MAE。同樣，我們重建（預測）柵格熱圖的最佳空間模型是具有 1.04198155 RMSE

和 0.3522904 MAE 的 9 批變分自動編碼器 (VAE)。鑑於這些發現，我們進一步使用數據可視化並為 STDM 任務中的每個域實施組合模型和架構。這項研究表明，這些現有模型可用於解決時空領域的預測問題。