2018 case study: Difference between revisions
Mr. MacKenty (talk | contribs) |
Mr. MacKenty (talk | contribs) |
||
| Line 7: | Line 7: | ||
== The case study == | == The case study == | ||
[[Media: | [[Media:2018 case study.pdf| Click here for the full pdf case study]] | ||
== Terms worth deeply understanding == | == Terms worth deeply understanding == | ||
Revision as of 11:29, 5 June 2017
Case study[1]
Introduction[edit]
This page will help you organize and understand the 2018 case study.
The case study[edit]
Click here for the full pdf case study
Terms worth deeply understanding[edit]
- Autonomous
- Backpropagation
- BigO notation
- Bounding boxes
- Brute-force
- Convolutional neural networks (CNNs)
- Cost function
- Deep learning
- Dijkstra’s algorithm
- End-to-end learning
- Feature maps (Activation maps)
- Filters (Kernels)
- Filter stride
- Greedy algorithm
- Machine learning
- Max-pooling / Pooling
- Multi-layer perceptron (MLP)
- Nearest neighbour algorithm
- Overfitting
- Point clouds
- Receptive field
- Sensor Fusion
- Society of Automotive Engineers
- Shift invariance (Spatial invariance)
- Vehicle-to-vehicle (VTV) protocol
- Vehicle-to-infrastructure (VTI) protocol
Previous years case study and test[edit]
- Click here for the 2017 case study
- Click here for the 2016 case study test
- Click here for the 2016 case study