Advanced Deep Learning Preview

Month 2 Elective: Advanced Deep Learning

Udacity has partnered with the NVIDIA Deep Learning Institute to build an advanced course on deep learning. This module covers semantic segmentation, and inference optimization. Both of these topics are active areas of deep learning research.

Semantic segmentation identifies free space on the road at pixel-level granularity, which improves decision-making ability. Inference optimizations accelerate the speed at which neural networks can run, which is crucial for computational-intense models like the semantic segmentation networks you’ll study in this module.

Advanced Deep Learning Lesson 1: Fully Convolutional Networks

In this lesson, you’ll build and train fully convolutional networks that output an entire image, instead of just a classification. You’ll implement three special techniques that FCNs use: 1x1 convolutions, upsampling, and skip layers, to train your own FCN models.

Advanced Deep Learning Lesson 2: Scene Understanding

In this lesson, you’ll learn the strengths and weaknesses of bounding box networks, like YOLO and Single Shot Detectors. Then you’ll go a step beyond bounding box networks and build your own semantic segmentation networks. You’ll start with canonical models like VGG and ResNet. After removing their final, fully-connected layers, you can add the three special techniques you’ve already practiced: 1x1 convolutions, upsampling, and skip layers. Your result will be an FCN that classifies each road pixel in the image!

Advanced Deep Learning Lesson 3: Inference Optimizations

One of the challenges of semantic segmentation is that it requires a lot of computational power. In this lesson, you’ll learn how to accelerate network performance in production, using techniques such as fusion, quantization, and reduced precision.

Advanced Deep Learning Project: Semantic Segmentation

In the project at the end of the Advanced Deep Learning Module, you’ll build a semantic segmentation network to identify free space on the road. You’ll apply your knowledge of fully convolutional networks and their special techniques to create a semantic segmentation model that classifies each pixel of free space on the road. You’ll accelerate the network’s performance using inference optimizations like fusion, quantization, and reduced precision. You’ll be studying and implementing approaches used by top performers in the KITTI Road Detection Competition!