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
Markus Koskela (CSC), Mats Sjöberg (CSC)
Language: English
Price: Free of charge
