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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。

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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:儘管很多NoSQL 數據庫近幾年大放異彩,但是像MySQL 這樣的關係型數據庫依然是互聯網的主流數據庫之一,每個學Python 的都有必要學好一門數據庫,不管你是做數據分析,還是網絡爬蟲,Web 開發、亦或是機器學習,你都離不開要和數據庫打交道,而MySQL 又是最流行的一種數據庫,這篇文章介紹Python 操作MySQL 的幾種方式,你可以在實際開發過程中根據實際情況合理選擇。

 

摘要: 銀行貸款員需要分析數據,以便搞清楚哪些貸款申請者是“安全”那些是“有風險”的。銷售經理需要數據分析,以便幫助他猜測哪些顧客會購買計算機。再或者醫學研究人員需要分析乳腺癌數據,以便預測病人應當接受三種治療中的哪一種。在上面的例子中,數據分析任務都是分類,都需要構造一個模型來預測一個類別型數據。譬如安全或者不安全、會購買與不會購買、那種治療都是類別型。分類是一種重要的數據分析形式,它提取刻畫重要數據類的模型,用來預測(離散的、無序的)類標號。

摘要: 主要分析了大數據平台架構的生態環境,並主要以數據源、數據採集、數據存儲與數據處理四個方面展開分析與講解,並結合具體的技術選型與需求場景,給出了我個人對大數據平台的理解。

摘要: 這篇文章講解了何謂人工智慧,還有他所能和所不能。這篇文章很簡單的解釋了機器學習的盲區,還有創造人工智能的我們常常忽視的關鍵點。在採用人工智慧的預測時我們可能會出錯,在設計人工智慧的邏輯時我們可能會出錯,看完這篇文章,深入淺出的講解給你聽。

摘要: 自從Google 的人工智能AlphaGO 成為圍棋界的百勝將軍開始,AI(Artificial Intelligence,人工智能)這兩個英文字,剎那間成為科技業最熱門的關鍵字之一。而就在2017年初,早在AI 領域打下深厚底子的IBM Watson,除了打進一些數據服務公司、科技公司外,甚至進軍醫療領域,能夠依照病患資料判定青光眼,準確率高達95%。

摘要: 這幾年來,機器學習和數據挖掘非常火熱,它們逐漸為世界帶來實際價值。與此同時,越來越多的機器學習算法從學術界走向工業界,而在這個過程中會有很多困難。數據不平衡問題雖然不是最難的,但絕對是最重要的問題之一。

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