practical_dl_sep2019 - practical_dl_sep2019 - Training
|Päiväys:||12.09.2019 9:00 - 13.09.2019 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 fee covers all materials, lunches as well as morning and afternoon coffees.|
Payment can be made with electronic invoicing, credit card, or direct bank transfer. Note that for electronic invoicing you need the operator and e-invoicing address (OVT code) of your organization. Please also note that invoice reference is needed for electronic invoicing in your organization, so please have this available when registering.
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. TensorFlow 2, Keras, and PyTorch will be used in the exercise sessions. CSC's Notebooks environment will be used on the first day of the course, and Taito-GPU and the new Puhti-AI partition 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.
|9-11||Introduction to deep learning and to Notebooks|
|13-14:30||Image data and convolutional neural networks|
|14:30-16||Text data, recurrent neural networks, and attention|
|9-10:30||Deep learning frameworks, GPUs, batch jobs|
|10:30-12||Image classification exercises|
|13-14||Text categorization exercises|
|14-16||Cloud, using multiple GPUs|