Nvidia is all set to bring the Deep Learning Institute (DLI) to India for the first time. With the venue being IIT Bombay location in the heart of Powai, Mumbai, the program will be held on 5 December 2016. At the event, global experts will conduct hands-on training sessions in Artificial Intelligence, DL techniques and problem-solving for all levels of data scientists and developers. On the abovementioned date and venue, Abel Brown, a Worldwide Field Operations Solution Architect will be up as a trainer for Nvidia DLI.
Nvidia GeForce GTX 1060 Founder’s Edition Graphic Card : Quick Review and All You Need to Know
Through hands-on training sessions as well as online courses in collaboration with Coursera, Udacity, and Microsoft, Nvidia DLI has trained practitioners globally. The upcoming edition of DLI will be held along with the inaugural Nvidia GTCx. It will offer two modules, customised for beginners and intermediates.
Modules are based on solving real-world challenges, from building a self-driving car to training your computer to automatically detecting cancer, and led by global subject matter experts.
Beginners
Getting started with Deep Learning
This lab introduces the machine learning workflow and provides hands-on experience with using deep neural networks (DNN) to solve a real-world image classification problem. One can also see the benefits of GPU acceleration in the model training process.
Deep Learning for Object Detection
Building upon the foundational understanding of how deep learning is applied to image classification, this lab explores different approaches to the more challenging problem of detecting if an object of interest is present within an image and recognising its precise location within the image.
Intermediate
Deep Learning for Network Deployment
In this lab, participants will test three different approaches to deploying a trained DNN for inference. They will learn about the role of batch size in inference performance as well as various optimizations that can be made in the inference process and can also explore inference for a variety of different DNN architectures trained in other DLI labs