Organised by TÜBİTAK ULAKBİM and Middle East Technical University
Please click here to watch the video recordings of the workshop sessions
Date:
16 January 2024 (2 sessions)
Event Type: Workshop
Format: Online (Zoom)
Topic: ML using the PyTorch Library
Overview:
ML using the PyTorch Library is a one-day online workshop (2 sessions in total) on the fundamentals of PyTorch and PyTorch workflow. 1st session will cover a quick overview of PyTorch, and PyTorch workflow. The 2nd session consists of the implementation of Multilayer Perceptron (MLP)/Convolutional Neural Network (CNN).
Online sessions will be held via Zoom between 10:00-12:15 GMT+3 in the morning and 13:30-14:45 GMT+3 in the afternoon.
Agenda:
Please see the Timetable for detailed information. Times given in İstanbul, Türkiye (GMT+3:00)
16 January 2024
10:00 - 12:15
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PyTorch
-
PyTorch Workflow
-
Q&A
13:30 - 14:45
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MLP/CNN implementation and training
-
Q&A
Language: English
Duration: 1 Day, 2 Sessions
Target Audience: Academia (possible BSc., MSc. and Ph.D. Students) and industry.
Prerequisite(s):
- Familiarity with Python programming language
- Knowledge of basics of programming (variables and basic data types, loops, and conditionals).
- Preferably being an undergraduate or graduate level student (no restrictions on programmes)
- Performing necessary installations (Anaconda etc.)
Tools, libraries, and frameworks used: Python, Anaconda/Miniconda
Learning Objectives: By participating in this course, you will have information about the main factors of PyTorch, PyTorch tools, and a basic implementation of MLP/CNN in the PyTorch framework
About the instructors:
Feyza Yavuz is a graduate student and teaching assistant at Middle East Technical University, specializing in developing machine learning solutions, particularly in deep learning. Over the past three years, her focus has been on various computer vision challenges, including object detection, efficient implementation of ranking-based loss functions, and energy prediction for urban-scale buildings. Additionally, she has been a teaching assistant for Data Structures and Computer Networking courses for four semesters.
Adnan Harun Doğan is a graduate student and teaching assistant at Middle East Technical University. He is working on "incorporating increasing and decreasing combinatorial optimisation algorithms into deep learning pipelines using implicit differentiation" in Image Processing and Pattern Recognition Lab.
Contact: ncc@ulakbim.gov.tr
Acknowledgments
This event was supported by the EuroCC 2 project. This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 101101903. The JU receives support from the Digital Europe Programme and Germany, Bulgaria, Austria, Croatia, Cyprus, Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, France, Netherlands, Belgium, Luxembourg, Slovakia, Norway, Türkiye, Republic of North Macedonia, Iceland, Montenegro, Serbia.
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