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Prijava

 Objave - Znanost i suradnja  

Projekt EuroCC - radionica "Deep learning through examples using Keras"

Datum objave: 14.06.2022. | Objavi(o)/la: Dario Došen
Događaj: 21.06.2022.


starija objava >> << novija objava

Poštovane kolegice, poštovani kolege,

u okviru projekta "EuroCC - National Competence Centres in the Framework of EuroHPC" (https://www.eurocc-access.eu) u organizaciji Hrvatskog centra kompetencija za HPC (http://www.hpc-cc.hr) i FERIT-a Osijek (https://www.ferit.unios.hr/2021/), institucije članice Hrvatskog centra kompetencija za HPC, 21.06.2022. s početkom u 11:00 sati održat će se on-line radionica "Deep learning through examples using Keras". Radionicu će održati Rok Hribar s Instituta Jožef Stefan iz Ljubljane.

Više informacija o radionici možete pronaći u privitku ove e-pošte ili na poveznici https://tinyurl.com/2p8844jj.

Molimo Vas da do 20.06.2022. godine u 16:00 sati na poveznici  https://forms.gle/ehZA9MvYiZuYZWqX9 iskažete svoj interes za sudjelovanje na radionici unosom imena, prezimena, naziva institucije i e-pošte.

Na radionici mogu sudjelovati istraživači, nastavnici i zaposlenici znanstveno-istraživačkih i visokoobrazovnih institucija, te partneri iz industrije i javnog sektora, uključujući doktorante i studente.

Broj sudionika radionice nije ograničen, ali je potrebna prijava na radionicu putem navedene poveznice.

Poveznicu za sudjelovanje na radionici dobit ćete na vrijeme prije održavanja radionice.

Hvala i lijepi pozdrav,
prof.dr.sc. Goran Martinović, voditelj zadatka 5.2 projekta EuroCC

 

Workshop  

Deep learning through examples using Keras  

21 June 2022, 11:00 AM  

Rok Hribar  

Department of Computer Systems, Jožef Stefan Institute, Ljubljana, Slovenia

 

 

Abstract  

In the workshop you will learn how to properly use artificial neural networks through practical  examples from machine learning. In recent years, the use of deep learning has spread from  academic circles and found its place in industry, advertising, healthcare, banking, security and  so on. In the workshop, we will present the most useful neural network architectures and their  components. We will implement the models within the Colaboratory cloud service, which  already includes all the necessary software and enables easy code sharing and execution on  Google's high-performance infrastructure (GPU, TPU). Through examples, you will learn about  the main types of data we encounter in machine learning and how we use them as input or  output with neural networks. We will present guidelines that lead us in designing an appropriate  model and the common obstacles we encounter in deep learning, together with the typical  tricks how to circumvent those obstacles.  

Course content: design of the Keras library, neural networks training basics, useful  regularizations, time series modeling, convolutional neural networks, embeddings, learning  from heterogeneous data. Knowledge of the basics of machine learning and Python language  is recommended for participants.  

CV  

Rok Hribar is a researcher at Computer Systems department, Jožef Stefan Institute, Ljubljana,  Slovenia. Most recently he is involved in several projects that deal with applying artificial  intelligence and deep learning in real-world industrial practice with applications in device  condition monitoring, self-driving industrial carts and healthcare. However, his research focus  is most notably automatic differentiation of programs and gradient based optimization  especially when applied to machine learning and large-scale optimization. He applied this  methodology to various use cases including deep learning, time series forecasting,  representation learning, clustering, deep reinforcement learning, simulation based  optimization, similarity learning and others. His work found applications in genetics, molecular  dynamics, biomedical knowledge discovery, transportation, acoustics, logistics and others. He  lead numerous lectures and workshops especially on the practical aspects and use of modern  approaches in deep learning, customized machine learning and gradient based optimization.

 

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