Deep Learning Fundamentals (H8PM0S)
This HPE NVIDIA DLI workshop covers the foundations of deep learning and offers hands-on training in Image Classification, Object Detection, and Neural Network Deployment using popular frameworks.
This full-day workshop is ideal for developers, data scientists, and researchers looking to solve challenging problems with deep learning.
At the conclusion of this full-day workshop, you will be able to:
- Understand general terms and background of deep learning
- Leverage deep neural networks (DNN) within the deep learning workflow to solve a real-world image classification problem
- Train and evaluate an image segmentation network using TensorFlow
- Take three approaches for neural network deployment: DIGITS and Caffe, Caffe, through its Python API and NVIDIA TensorRT™
Deep Learning Lecture
Image Classification with DIGITS (hands-on lab)
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 will walk through the process of data preparation, model definition, model training and troubleshooting. You will use validation data to test and try different strategies for improving model performance using GPUs. On completion of this lab, you will be able to use DIGITS to train a DNN on your own image classification application.
Object Detection with DIGITS (hands-on lab)
This lab explores three approaches to identify a specific feature within an image. Each approach is measured in relation to three metrics: model training time, model accuracy and speed of detection during deployment. On completion of this lab, you will understand the merits
Neural Network Deployment with DIGITS and TensorRT (hands-on lab)
This lab will introduce three approaches for neural network deployment. The first approach teaches you to use inference functionality directly within a deep learning framework (NVIDIA DIGITS and Caffe). The second approach teaches you how to integrate inference within a custom application by using a deep learning framework API (Caffe, through its Python API). The final approach teaches you to use TensorRT, which will automatically create an optimized inference run-time from a trained Caffe model and network description file. As you explore these approaches, 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 will also explore inference for a variety of different DNN architectures trained in other DLI labs.