|Date:||13.02.2019 9:00 - 14.02.2019 16:00|
|Location details:||The event is organised at the CSC Training Facilities located in the premises of CSC at Keilaranta 14, Espoo, Finland. The best way to reach us is by public transportation; more detailed travel tips are available.|
|Lecturers:|| Markus Koskela (CSC) |
Mats Sjöberg (CSC)
|The course materials, lunches as well as morning and afternoon coffees are free of charge.|
We have processed a LIMITED WAITING LIST (10 persons max). Please send your request to firstname.lastname@example.org and we will keep those on the waiting list informed of whether participation will be possible. Please note that registration/wait list is at a first come, first served basis.
This course gives a practical introduction to deep learning, convolutional and recurrent neural networks, GPU computing, and tools to train and apply deep neural networks for natural language processing, images, and other applications.
The course consists of lectures and hands-on exercises. Keras (https://keras.io/) and PyTorch (https://pytorch.org/) will be used in the exercise sessions. CSC's Notebooks (https://notebooks.csc.fi/) environment will be used on the first day of the course, and the Taito-GPU (https://research.csc.fi/taito-gpu) cluster on the second day.
After the course the participants should have the skills and knowledge needed to begin applying deep learning for different tasks and utilizing the GPU resources available at CSC for training and deploying their own neural networks.
The participants are assumed to have working knowledge of Python and suitable background in data analysis, machine learning, or a related field. Previous experience in deep learning is not required, but the fundamentals of machine learning are not covered on this course. Basic knowledge of a Linux/Unix environment will be assumed.
Day 1, Wednesday 13.2
09.00 – 10.30 Lecture: Introduction to deep learning
10.30 – 11.00 Exercises: Introduction to Notebooks, Keras fundamentals
11.00 – 12.00 Lecture: Image data, multi-layer percepton networks, convolutional neural networks
12.00 – 13.00 Lunch
13.00 – 14.00 Exercises: Image classification with MLPs, CNNs
14.00 – 15.00 Lecture: Text data, embeddings, neural NLP, recurrent neural networks
15.00 – 16.00 Exercises: Text sentiment classification with CNNs, RNNs
Day 2, Thursday 14.2
09.00 – 10.00 Lecture: GPUs, batch jobs, using Taito-GPU
10.00 – 12.00 Exercises: Image classification
12.00 – 13.00 Lunch
13.00 – 14.00 Exercises: Text categorization and labelling
14.00 – 15.00 Lecture: Cloud, GPU utilization, multiple GPUs
15.00 – 16.00 Exercises: Using multiple GPUs
Coffee will be served both for the morning and afternoon sessions