Hluboké učení pro analýzu velkých dat
| Thesis title in Czech: | Hluboké učení pro analýzu velkých dat |
|---|---|
| Thesis title in English: | Deep learning for big data analytics |
| Key words: | Hluboké učení, big data, distribuované výpočty |
| English key words: | Deep learning, big data, distributed computing |
| Academic year of topic announcement: | 2018/2019 |
| Thesis type: | dissertation |
| Thesis language: | |
| Department: | Department of Software Engineering (32-KSI) |
| Supervisor: | doc. RNDr. Jakub Lokoč, Ph.D. |
| Author: |
| Guidelines |
| Analysis of big data and deep learning are two intensively investigated research topics with high practical impact. Whereas huge amounts of domain-specific data contain useful additional information for decision making processes, deep learning methods focuse on high-level complex abstractions given unsupervised data. The goal of this work is to address some important problems in big data analytics using deep learning approaches. For example, extract/analyze complex patterns from huge volumes of unstructured data, data annotation or effective/efficient information retrieval. |
| References |
| Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, and Edin Muharemagic. Deep learning applications and challenges in big data analytics. Journal of Big Data, 2015.
Suthaharan S: Big data classification: Problems and challenges in network intrusion prediction with machine learning. In ACM Sigmetrics: Big Data Analytics Workshop. ACM, Pittsburgh, PA; 2013. Bengio Y: Learning Deep Architectures for AI. Now Publishers Inc., Hanover, MA, USA; 2009. Holubová Irena, Kosek Jiří, Minařík Karel, Novák David. Big Data a NoSQL databáze. Grada Publishing, a.s., 2015. |