SGInnovate partners with NVIDIA Deep Learning Institute (DLI) to offer hands-on training to developers, data scientists, and researchers looking to solve real world problems with deep learning, across diverse industries such as self-driving cars, healthcare, online services, and robotics.
NVIDIA DLI and SGlnnovate are excited lo announce the full-day practical deep learning workshops that covers the foundations of deep learning and offers hands-on training in Image Classification, Object Detection, and Neural Network Deployment using popular frameworks.
For more information on the upcoming training dates, please visit our Events page.
Lab #1: Image Classification with DIGITS
Learn how to leverage deep neural networks (DNN) within the deep learning workflow to solve a real-world image classification problem using NVIDIA DIGITS. You’ll walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance using GPUs. On completion of this lab, you will be able to use NVIDIA DIGITS to train a DNN on your own image classification application.
Lab #2: Deep Learning for Image Segmentation
Abstract: There are a variety of important applications that need to go beyond detecting individual objects within an image and instead segment the image into spatial regions of interest. Examples of image segmentation uses include medical imagery analysis where it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells so we can isolate a particular organ and self-driving cars where it is used to understand road scenes. In this lab you’ll learn how to train and evaluate an image segmentation network.
Lab #3: Neural Network Deployment with DIGITS and TensorRT
Abstract: Once a deep neural network (DNN) has been trained using GPU acceleration, it needs to be deployed into production. The step after training is called inference as it uses a trained DNN to make predictions from new data.
In this lab we will show different approaches to deploying a trained DNN for inference. The first approach is to directly use inference functionality within a deep learning framework, in this case DIGITS and Caffe. The second approach is to integrate inference within a custom application by using a deep learning framework API, again using Caffe but this time through it’s Python API. The final approach is to use the NVIDIA TensorRT™ which will automatically create an optimized inference run-time from a trained Caffe model and network description file. You will learn about the role of batch size in inference performance as well as various optimizations that can be made in the inference process. You’ll also explore inference for a variety of different DNN architectures trained in other DLI labs.
You will receive a Beginner Level certificate from the Deep Learning Institute!
Read more about NVIDIA Deep Learning Institute (DLI) here.