[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117151-en":3,"doc-seo-117151-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117151,137441390410,"Hazel","https://ap-avatar.wpscdn.com/avatar/2000252f4ab5702993?_k=1776741390130283984",8,"Research & Report","Machine learning and deep learning","Intelligent systems delivering artificial intelligence capabilities increasingly depend on machine learning, which enables systems to learn from problem-specific training data to automate analytical model building and task execution. Deep learning builds on artificial neural networks and, across many applications, can outperform shallow machine learning and traditional data analysis. This article synthesizes core fundamentals, clarifies key terms and conceptual distinctions, describes automated analytical model building, and examines implementation challenges in electronic markets, including non-technical issues such as human-machine interaction and AI servitization.","Electronic Markets (2021) 31:685–695  \n[https://doi.org/10.1007/s12525-021-00475-2](https://doi.org/10.1007/s12525-021-00475-2)  \nMachine learning and deep learning  \nChristian Janiesch 1  & Patrick Zschech 2  & Kai Heinrich 3  \nReceived: 7 October 2020 /Accepted: 19 March 2021 / Published online: 8 April 2021  \n\\# The Author(s) 2021, corrected publication 2021  \nAbstract  \nToday, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.  \nKeywords Machine learning . Deep learning . Artificial intelligence . Artificial neural networks . Analytical model building  \nJEL classification C6 . C8 . M15 . O3  \nIntroduction  \nIt is considered easier to explain to a child the nature of what constitutes a sports car as opposed to a normal car by showing him or her examples, rather than trying to formulate explicit rules that define a sports car.  \nSimilarly, instead of codifying knowledge into computers, machine learning (ML) seeks to automatically learn  \nResponsible Editor: Fabio Lobato  \n* Christian Janiesch [christian.janiesch@uni-wuerzburg.de](christian.janiesch@uni-wuerzburg.de)  \nPatrick Zschech  \n[patrick.zschech@fau.de](patrick.zschech@fau.de)  \nKai Heinrich  \n[kai.heinrich@ovgu.de](kai.heinrich@ovgu.de)  \n1 Faculty of Business Management & Economics, University of Würzburg, Sanderring 2, 97070 Würzburg, Germany  \n2 Institute of Information Systems, Friedrich-Alexander University Erlangen-Nürnberg, Lange Gasse 20, 90403 Nürnberg, Germany  \n3 Faculty of Economics and Management, Otto-von-Guericke-Universität Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany  \nmeaningful relationships and patterns from examples and observations (Bishop 2006) . Advances in ML have enabled the recent rise of intelligent systems with human-like cognitive capacity that penetrate our business and personal life and shape the networked interactions on electronic markets in every conceivable way, with companies augmenting decisionmaking for productivity, engagement, and employee retention (Shrestha et al. 2021), trainable assistant systems adapting to individual user preferences (Fischer et al. 2020), and trading agents shaking traditional finance trading markets (Jayanth Balaji et al. 2018) .  \nThe capacity of such systems for advanced problem solving, generally termed artificial intelligence (AI), is based on analytical models that generate predictions, rules, answers, recommendations, or similar outcomes. First attempts to build analytical models relied on explicitly programming known relationships, procedures, and decision logic into intelligent systems through handcrafted rules (e.g., expert systems for medical diagnoses) (Russell and Norvig 2021) . Fueled by the practicability of new programming frameworks, data availability, and the broad access to necessary computing power, analytical models are nowadays increasingly built using what is generally referred to as ML (Brynjolfss","cbCaiqsaRPJdgLwQ","https://ap.wps.com/l/cbCaiqsaRPJdgLwQ","pdf",413075,1,11,"English","en",105,"# Introduction\n## Machine learning concepts and learning from data\n## Evolution to deep learning and neural network architectures\n## Challenges in real business implementation\n## Goal and scope for electronic markets","[{\"question\":\"What is the core idea of machine learning in intelligent systems?\",\"answer\":\"Machine learning enables systems to learn relationships and patterns from problem-specific training data, automating analytical model building and solving associated tasks.\"},{\"question\":\"How does deep learning differ from traditional machine learning?\",\"answer\":\"Deep learning is based on artificial neural networks and uses deeper architectures; for many applications it can outperform shallow machine learning and conventional data analysis approaches.\"},{\"question\":\"What implementation challenges are discussed for machine learning and deep learning in electronic markets?\",\"answer\":\"Key challenges include selecting among many implementation options, handling bias and drift in data, mitigating black-box properties, and managing the reuse of preconfigured models provided as a service.\"}]","Machine learning and deep learning | PDF",1785674121,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-and-deep-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-deep-learning/117151/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the core idea of machine learning in intelligent systems?","Question",{"text":75,"@type":76},"Machine learning enables systems to learn relationships and patterns from problem-specific training data, automating analytical model building and solving associated tasks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does deep learning differ from traditional machine learning?",{"text":80,"@type":76},"Deep learning is based on artificial neural networks and uses deeper architectures; for many applications it can outperform shallow machine learning and conventional data analysis approaches.",{"name":82,"@type":73,"acceptedAnswer":83},"What implementation challenges are discussed for machine learning and deep learning in electronic markets?",{"text":84,"@type":76},"Key challenges include selecting among many implementation options, handling bias and drift in data, mitigating black-box properties, and managing the reuse of preconfigured models provided as a service.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]