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

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

轉貼自： Finance BigData.eu

摘要： 去年年末的那段時間裡，看了很多天池大賽里面得高分的選手的算法思路，大概總結了有監督學習中的一些核心流程及重要細節

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.

摘要： 前言：本文主要涉及知識點包括新浪微博爬蟲、python對數據庫的簡單讀寫、簡單的列表數據去重、簡單的自然語言處理(snowNLP模塊、機器學習)。適合有一定編程基礎，並對python有所了解的盆友閱讀。

摘要： 日前，KDnuggets 上的一篇文章總結了七十多個免費的數據集，內容涉及到政府、金融、衛生、新聞傳媒等各個方面，除了這些數據，文中還提供數據提取地址。我們將文章編譯整理如下。

摘要： 筆記本電腦、智能手機、傳感器，都為物聯網帶來了大量數據。這是獲得競爭優勢（或者保持競爭力）的重大機遇，前提是企業足夠靈活，可以管理好數據並把數據變​​成有用的商業智能。 人腦能高效地處理視覺圖像。在這個過程中，大腦使用了潛意識，讓決策者可以通過迅速掃描圖像來處理信息。可視化圖表利用了大腦的圖像識別能力，出色的可視化模型將成為處理龐大數據集的更好選擇，也是2018年重要的大數據趨勢之一。

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： 本文為美國著名數據分析網站DZone分析師Tom Smith與Exaptive的副總裁Matt Coatney的專訪對話，對人工智能和機器學習的未來做了深度的探討.Exaptive是一家美國俄克拉荷馬州以提供大數據分析產品及服務為主的初創企業。