Every year NVIDIA’s GPU Technology Conference (GTC) gets bigger and better. One of the aims of GTC is to give developers, scientists, and practitioners opportunities to learn with hands-on labs how to use accelerated computing in their work. This year we are nearly doubling the amount of hands-on training provided from last year, with almost 2,400 lab hours available to GTC attendees!
We have two types of training this year at GTC: instructor-led labs and self-paced labs. And to help you keep up with one of the hottest trends in computing, this year we’re featuring a Deep Learning training track. Keep reading for details. If you haven’t registered for GTC yet this year, keep reading for a discount code.
Deep Learning Track
There is an explosion of Deep Learning topics at GTC, and it’s not limited to the keynotes, talks and tutorial sessions. We’ll feature at least six hands-on labs related to accelerating facets of Deep Learning on GPUs. From an introduction to Deep Learning on GPUs to cutting-edge techniques and tools, there will be something for everyone. Be sure to get to these labs early to get yourself a seat! Here are a few of the labs available in this track:
- Introduction to Machine Learning with GPUs: Handwritten digit classification (S5674)
- DIY Deep Learning for Vision with Caffe (S5647)
- Applied Deep Learning for Vision, Natural Language and Audio with Torch7 (S5574)
- Deep Learning with the Theano Python Library (S5732)
- Deep Belief Networks Using ArrayFire (S5722)
- Accelerate a Machine Learning C++ example with Thrust (S5822)
Just like GTC last year, there will be twenty hands-on instructor-led labs. These are 80-minute labs led by an expert on the topic. Continue reading