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摘要: 考特尼·威爾遜是CloudFactory營銷總監。最近發表了篇關於人工智能的文章,雖然許多人正在尋找“殺手級”的視覺,但更有可能視覺是AI和計算機的“殺手級應用”。

摘要: 「贏者詛咒」:贏得拍賣品的中標者出價高於其他競標者,但他很可能對拍賣品估價過高,支付了超過其價值的價格,從而贏得的拍賣品的收益會低於正常收益甚至爲負。換句話說,就是當你一心想要贏得競標時,卻偏離了你原本的目的:當我們爲各種測評任務中取得的分數歡欣鼓舞時,可能我們已經受到了「贏者詛咒」。

摘要: 今天在學校又雙提到了 Deep Reinforcement Learning That Matters 這篇打響 DRL(Deep Reinforcement Learning, 深度強化學習)勸退第一槍的文章後,回來以後久違刷了一下推特,看到了這篇爆文 Deep Reinforcement Learning Doesn't Work Yet,或可直譯爲深度強化學習還玩不轉或意譯爲深度強化學習不能即插即玩。

摘要: 創建一個爬蟲項目,以圖蟲網為例抓取裡面的圖片。在頂部菜單“發現” “標籤”裡面是對各種圖片的分類,點擊一個標籤,我們以此作為爬蟲入口,分析一下該頁面

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Big data has both high volume and high velocity – one way this manifests is as silos of in-situ data representing departments in banks that are very difficult to move and integrate to obtain a single coherent customer view. Further, the ability to perform data analytics – dynamically and in near real-time – of rapidly changing customer and market data is increasingly critical for competitiveness. By considering the distributed nature of financial data storage and the velocity of financial markets, the objective of this RP is to develop distributed and real-time machine learning methods to identify decentralised and dynamic models for financial analysis, prediction, and risk management.

This project will develop (i) methods to identify cross-effects between different data resources, regions, sectors, and markets, (ii) distributed versions of methods to identify decentralised models that include individual local model components learned from local resources and cross-impact model components learned from data resources in other regions/sectors/markets, and (iii) real-time learning methods to update decentralised models and address financial market velocity.

Based on the distributed and cloud computing infrastructure, this approach should address the weakness of existing data-centralised and off-line machine learning methods, which fail to consider the cost of data transportation, storage, and fast timevarying characteristics of financial markets. The originality of this approach is its dynamic integration, by distributed and real-time mining, to maximise the effectiveness and efficiency of big data analysis.

Early Stage Resercher working on the project: Sergio Garcia Vega

Supervisor: Professor John Keane, University of Manchester / john.keane(at)manchester.ac.uk

轉貼自: Finance BigData.eu

摘要: 通過深度學習技術,物聯網(IoT)設備能夠得以解析非結構化的多媒體數據,智能地響應用戶和環境事件,但是卻伴隨著苛刻的性能和功耗要求。本文作者探討了兩種方式以便將深度學習和低功耗的物聯網設備成功整合。

摘要: Despite big data currently ranking among top business intelligence and data analytics trends, businesses continue to suffer from a lack of data-savvy talent. Research from BARC shows half of respondents reporting a lack of analytical or technical know-how for big data analytics. This is good news for tech beginners, however, whose knowledge and skills are being welcomed by companies who want to reap the benefits of big data.

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