[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117017-en":3,"doc-seo-117017-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117017,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Study of Variational Autoencoders in Machine Learning - Report","Autoencoders are central to machine learning due to their ability to learn compact, effective representations of complex inputs. They support unsupervised tasks such as data generation, dimensionality reduction, and anomaly detection without relying on explicit labels. By encoding inputs into a lower-dimensional latent space, autoencoders also enable practical data compression under storage or bandwidth constraints. The project further frames autoencoders within generative modeling for synthetic data closely matching the training distribution, including applications like image generation.","Study of Variational Autoencoders in Machine Learning  \nDocument:  \nReport  \nAuthor:  \nJoel Campo Moy`a  \nDirector/Co-director:  \n`Alex Ferrer Ferre  \nDegree:  \nBachelor’s degree in Aerospace Vehicle Engineering  \nExamination session:  \nSpring  \nAbstract  \nAutoencoders are essential in the field of machine learning because of the wide range of applications and distinctive talents they have. The ability of autoencoders to learn condensed and effective representations of complicated input data is one of the main factors in their significance. Autoencoders offer effective data compression by encoding the input data into a lower-dimensional latent space, which is useful in situations with constrained storage or bandwidth. Autoencoders are also frequently employed for unsupervised learning tasks like data generation, dimensionality reduction, and anomaly detection. Without relying on explicit labels or supervision, they enable us to find underlying patterns and structures in the data. Overall, the versatility and utility of autoencoders make them a fundamental tool in the machine learning toolbox, empowering researchers and practitioners to tackle a wide range of problems across diverse domains.  \nGenerative models, such as autoencoders, play a fundamental role in machine learning by enabling the creation of new, synthetic data that closely resembles the original input distribution. These models have revolutionised various domains, including image generation, text synthesis, and music composition, among many others. By capturing the underlying patterns and structures of the training data, generative models provide a powerful framework for creative applications, data augmentation, and simulation studies.  \nThis project aimed to explore the capabilities and applications of autoencoders, a type of neural network architecture, in the field of machine learning. The main focus of the project was to refactor legacy code used for image identification and transform it into an autoencoder capable of generating MNIST images. Through extensive experimentation and analysis, the project demonstrated the effectiveness of autoencoders in learning representations of input data and generating high-quality synthetic images. The findings of this study contribute to our understanding of autoencoders and their potential for various tasks, including image generation. The project also highlighted the importance of clean code practises, code refactoring, and neural network architectural design principles in adapting existing models for new purposes.  \nResum  \nEls Autoencoders s´on essencials en el camp de l’aprenentatge autom`atic a causa de l’ampli ventall d’aplicacions i talents distintius que tenen. La capacitat dels autoencoders per aprendre representacions de dades complicadesi que no tenen per qu`e estar perfectes ´es un dels principals factors de la seva import`ancia. Els autoencoders ofereixen una compressi´o de dades efica¸c mitjan¸cant la codificaci´o de les dades d’entrada en un espai latent de dimensions inferiors, cosa que ´es ´util en situacions amb emmagatzematge o amplada de banda restringida. Els autoencoders tamb´e s’utilitzen amb freq¨u`encia per a tasques d’aprenentatge no supervisades com la generaci´ode dades, la reducci´o de la dimensionalitat i la detecci´o d’anomalies. Sense dependre d’etiquetes o supervisi´oexpl´ıcites, ens permeten trobar patrons i estructures subjacents a les dades. En general, la versatilitat i la utilitat dels autoencoders els converteixen en una eina fonamental en l’aprenentatge autom`atic, que permetals investigadors i professionals d’abordar una `amplia gamma de problemes en diversos dominis.  \nEls models generatius, com els autoencoders, tenen un paper fonamental en l’aprenentatge autom`atic, jaque permeten la creaci´o de dades noves i sint`etiques que s’assemblen molt a la distribuci´o d’entrada original. Aquests models han revolucionat diversos `ambits, com ara la generaci´o d’imatges, la s´ıntesi de tex","cbCaivDYNxA8TYQY","https://ap.wps.com/l/cbCaivDYNxA8TYQY","pdf",5541423,1,69,"English","en",105,"# Introduction\n## Autoencoders and generative modeling\n# Project objective and approach\n## Refactoring legacy code for MNIST\n## Experiments and analysis\n# Key takeaways\n## Representation learning and synthetic image quality\n## Code refactoring and architectural design","[{\"question\":\"What makes autoencoders important in machine learning?\",\"answer\":\"Autoencoders learn compact latent representations of complex data, enabling data compression and supporting multiple unsupervised tasks without explicit supervision.\"},{\"question\":\"What tasks do autoencoders commonly enable?\",\"answer\":\"They are used for data generation, dimensionality reduction, and anomaly detection, helping reveal underlying patterns and structures in the data.\"},{\"question\":\"What was the main goal of this project?\",\"answer\":\"To refactor legacy image-identification code into an autoencoder that can generate MNIST images, validating its effectiveness through experiments and analysis.\"}]",1785673096,174,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"study-of-variational-autoencoders-in-machine-learning-report","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/study-of-variational-autoencoders-in-machine-learning-report/117017/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What makes autoencoders important in machine learning?","Question",{"text":74,"@type":75},"Autoencoders learn compact latent representations of complex data, enabling data compression and supporting multiple unsupervised tasks without explicit supervision.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What tasks do autoencoders commonly enable?",{"text":79,"@type":75},"They are used for data generation, dimensionality reduction, and anomaly detection, helping reveal underlying patterns and structures in the data.",{"name":81,"@type":72,"acceptedAnswer":82},"What was the main goal of this project?",{"text":83,"@type":75},"To refactor legacy image-identification code into an autoencoder that can generate MNIST images, validating its effectiveness through experiments and 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