Mar 21, 2018
Bryan Catanzaro, the VP
Applied Deep Learning Research at NVIDIA, joins Mark and Melanie this week to discuss how
his team uses applied deep learning to make NVIDIA products and
processes better. We talk about parallel processing and compute
with GPUs as well as his team’s research in graphics, text and
audio to change how these forms of communication are created and
rendered by using deep learning.
This week we are also joined by a special co-host, Sherol Chen who is a developer
advocate on GCP and machine learning researcher on Magenta at
Google. Listen at the end of the podcast where Mark and Sherol chat
about all things GDC.
Bryan Catanzaro
Bryan Catanzaro is VP of Applied Deep Learning Research at
NVIDIA, where he leads a team solving problems in domains ranging
from video games to chip design using deep learning. Bryan earned
his PhD from Berkeley, where he focused on parallel computing,
machine learning, and programming models. He earned his MS and BS
from Brigham Young University, where he worked on higher radix
floating-point representations for FPGAs. Bryan worked at Baidu to
create next generation systems for training and deploying deep
learning models for speech recognition. Before that, he was a
researcher at NVIDIA, where he worked on programming models for
parallel processors, as well as libraries for deep learning, which
culminated in the creation of the widely used CUDNN library.
Cool things of the week
- NVIDIA Tesla V100s coming to Google Cloud site
- Automatic Serverless Deployment with Cloud Source Repositories
blog
- Magenta site
- NSynth Super site
- MusicVAE site
- Making music using new sounds generated with machine learnnig
blog
- Building Blocks of Interpretability blog
Interview
- NVIDIA site
- NVIDIA GPU Technology Conference (GTC) site
- CUDA site
- cuDNN site
- NVIDIA Volta site
- NVIDIA Tesla P4
docs
- NVIDIA Tesla V100s site
- Silicon Valley AI Lab Baidu Research site
- ICML: International Conference on Machine Learning site
- CVPR: Computer Vision and Pattern Recognition Conference
site
Referenced Papers & Research:
- Deep learning with COTS HPC System
paper
- Building High-level Features Using Large Scale Unsupervised
Learning paper
- OpenAI Learning to Generate Reviews and Discovering Sentiment
paper
- Progressive Growing of GANs for Improved Quality, Stability,
and Variation paper and CelebA
dataset
- High-Resolution Image Synthesis and Semantic Manipulation with
Conditional GANs paper
- Deep Image Prior site
- How a Japanese cucumber farmer is using deep learning and
TensorFlow
blog
Sample Talks:
- Future of AI Hardware Panel video
- High Performance Computing is Supercharging AI blog/video
- AI Podcast: Where is Deep Learning Going Next?
blog/video
Sample Resources:
- Coursera How Google does Machine Learning site
- NVIDIA Deep Learning Institute site
- Udacity AI Nanodegree
site
- Kaggle site
- TensorFlow site
- PyTorch site
- Keras site
Question of the week
What to watch out for and get involved in at the Game Developers
Conference (GDC) this year and in the future?
- International Grame Developers Association (IGDA) site
- Fellowship of GDC Parties site
- ALtCtrlGDC site
- Experimental Gameplay Workshop site
- Women in Games International (WIGI) site
- Blacks in Gaming (BIG) site
- Serious Games (SIGs) site
- What’s New in Firebase and Google Cloud Platform for Games
site
- Summits to Checkout:
- AI Game Developers Summit site
- Game Narrative Summit site
- Independent Games Summit site
- Additional Advice:
- The first two days are summits which are great because topic
focused
- Expo floor takes a good hour to get through
- WIGI, BIG and SIGs (Google and Microsoft) have the best
food
- GDC is composed of various communities
- Bring business cards
- Check out post-mortems
- Favorite Games:
- Games Mark & Sherol are currently playing:
- Hearthstone site
- Dragon Age Origins wiki
Where can you find us
next?
Mark and Sherol are at the Game
Developer’s Conference (GDC). You can find them via the Google
at GDC 2018 site.
Sherol will be at TensorFlow Dev Summit
speaking about machine learning research and creativity next
week.