Pub Date : 2021-12-01DOI: 10.1142/9789811241086_0007
R. Parr, Kris K. Hauser
This course provides the knowledge to construct and use deep neural networks for image and text analysis. The course starts from the basic concepts to understand, train and test neural networks for classification and regression. It introduces image analysis and then evolves to (Fully) Convolutional Neural Networks for image classification, object detection, and (semantic/instance) segmentation. In the sequence, it provides an introduction to text analysis and then covers Recurrent Neural Networks, Attention, Transformers and applications in text analysis. Prior knowledge in optimization, linear algebra, statistics, machine learning, image/text processing and analysis is important, but the basic concepts are provided whenever they are required. It is important the student can code in Python and desirable prior knowledge in PyTorch, and other packages usually used in python scripts for image and text processing, graphics display, and machine learning.
{"title":"Deep Learning","authors":"R. Parr, Kris K. Hauser","doi":"10.1142/9789811241086_0007","DOIUrl":"https://doi.org/10.1142/9789811241086_0007","url":null,"abstract":"This course provides the knowledge to construct and use deep neural networks for image and text analysis. The course starts from the basic concepts to understand, train and test neural networks for classification and regression. It introduces image analysis and then evolves to (Fully) Convolutional Neural Networks for image classification, object detection, and (semantic/instance) segmentation. In the sequence, it provides an introduction to text analysis and then covers Recurrent Neural Networks, Attention, Transformers and applications in text analysis. Prior knowledge in optimization, linear algebra, statistics, machine learning, image/text processing and analysis is important, but the basic concepts are provided whenever they are required. It is important the student can code in Python and desirable prior knowledge in PyTorch, and other packages usually used in python scripts for image and text processing, graphics display, and machine learning.","PeriodicalId":143059,"journal":{"name":"Fintech for Finance Professionals","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114139815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1142/9789811241086_0008
Doug Blank
This chapter explains basic computer networking concepts and technology and also introduces computer networking terminology to help you understand the uses and benefits of Novell products. We believe this primer provides a fairly thorough foundation for understanding any computer networking–related discussion. Understanding computer networking technology illuminates its potential. It also enables one to understand the viability of global networking, which will eventually provide any computer user a digital link to all other computer users and to digital information and services worldwide.
{"title":"Computer Network","authors":"Doug Blank","doi":"10.1142/9789811241086_0008","DOIUrl":"https://doi.org/10.1142/9789811241086_0008","url":null,"abstract":"This chapter explains basic computer networking concepts and technology and also introduces computer networking terminology to help you understand the uses and benefits of Novell products. We believe this primer provides a fairly thorough foundation for understanding any computer networking–related discussion. Understanding computer networking technology illuminates its potential. It also enables one to understand the viability of global networking, which will eventually provide any computer user a digital link to all other computer users and to digital information and services worldwide.","PeriodicalId":143059,"journal":{"name":"Fintech for Finance Professionals","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121561641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2012-01-02DOI: 10.1002/9781119205005.CH1
Frank J. Ohlhorst
{"title":"Big Data","authors":"Frank J. Ohlhorst","doi":"10.1002/9781119205005.CH1","DOIUrl":"https://doi.org/10.1002/9781119205005.CH1","url":null,"abstract":"","PeriodicalId":143059,"journal":{"name":"Fintech for Finance Professionals","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115860333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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