dlintro_1_2018 - Training
|Date:||17.04.2018 9:00 - 18.04.2018 17:00|
|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.|
|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. Keras and TensorFlow will be used in the exercise sessions. CSC's Notebooks environment will be used on the first day of the course, and the Taito-GPU cluster on the second day.
After the course the participants should have the skills needed to begin applying deep learning for different tasks and utilizing the available GPU capacity 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. Basics of linear algebra and calculus are sufficient. 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.
Course materials can be found at https://github.com/csc-training/intro-to-dl .
Schedule (changes are possible):
Day 1: Notebooks
9-10 Lecture: Introduction to deep learning
10-11 Exercise: Introduction to Notebooks, Keras fundamentals
11-12 Lecture: Image data, multi-layer percepton networks, convolutional neural networks
13-14 Exercise: Image classification with MLPs, CNNs
14-15 Lecture: Text data, embeddings, neural NLP, recurrent neural networks
15-16 Execise: Text sentiment classification with CNNs, RNNs
Day 2: Taito-GPU
9-10: Lecture: GPUs, batch jobs, using Taito-GPU