Computer Vision and Perception for Self-Driving Cars (Deep Learning Course)

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We considered the value of this course from many aspects, and finally summarized it for you from two aspects: skills and knowledge, and the people who benefit from it:
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What skills and knowledge will you acquire during this course?
During this course on Computer Vision and Perception for Self-Driving Cars, the learner will acquire the following skills and knowledge:
1. Fully Convolutional Network (FCN): The learner will understand the concept of FCN and its application in road segmentation. They will also learn about the Kaggle dataset and notebook, KITTI dataset, and the FCN paper.
2. YOLO (You Only Look Once): The learner will gain knowledge about 2D object detection using YOLO. They will explore the Kaggle competition and dataset, visualization notebook, YOLO notebook, and the fundamentals of object detection playlist. They will also be introduced to the YOLO paper.
3. Deep SORT (Simple Online and Realtime Tracking): The learner will learn about object tracking using Deep SORT. They will be provided with the dataset, notebook/code, and a blog on Deep SORT. Additionally, they will understand concepts like the Kalman filter, Hungarian algorithm, cosine distance metric, Mahalanobis distance, and the YOLO algorithm.
4. KITTI 3D Data Visualization and Homogenous Transformations: The learner will gain knowledge about visualizing 3D data from the KITTI dataset using homogenous transformations. They will be provided with the dataset, notebook/code, and information about LIDAR technology. They will also learn why Tesla doesn't use LIDAR.
5. Multi Task Attention Network (MTAN) and Multi Task Learning: The learner will understand the concept of MTAN and its application in multi-task learning. They will explore the dataset, notebook/code, data visualization, and the MTAN paper. They will also be provided with a blog on multi-task learning and information about image segmentation and FCN.
6. SFA 3D (Sparse Feature Aggregation for 3D Object Detection): The learner will gain knowledge about 3D object detection using SFA 3D. They will be provided with the dataset, notebook/code, data visualization, and a video on data visualization. They will also learn about feature pyramid networks, keypoint feature pyramid network, heat maps, focal loss, L1 loss, balanced L1 loss, learning rate decay, and cosine annealing.
7. UNetXST (UNet with Extra Skip Connections and Spatial Transformer Networks): The learner will learn about transforming camera images to bird's eye view using UNetXST. They will explore the dataset, dataset visualization, notebook/code, UNetXST paper, UNetXST GitHub repository, UNet, image transformations, and spatial transformer networks.
Who will benefit from this course?
This course on Computer Vision and Perception for Self-Driving Cars will benefit individuals and professionals who are interested in or working in the field of autonomous vehicles and robotics. Specifically, the following groups will benefit from this course:
1. Robotics Engineers: This course provides in-depth knowledge and practical skills in computer vision and perception, which are essential for developing self-driving cars. Robotics engineers will gain a comprehensive understanding of the different tasks involved in the perception unit of a self-driving car.
2. Machine Learning Engineers: Deep learning techniques, such as convolutional neural networks (CNNs), are extensively used in computer vision tasks. Machine learning engineers will benefit from learning about CNNs and their applications in road segmentation, object detection, and 3D object detection.
3. Autonomous Vehicle Researchers: Researchers working on autonomous vehicles can enhance their knowledge of advanced perception algorithms and techniques through this course. They will learn about state-of-the-art methods like YOLO (You Only Look Once), Deep SORT (Simple Online and Realtime Tracking), and Multi Task Attention Network (MTAN) for various perception tasks.
4. Data Scientists: Data scientists interested in computer vision and perception can gain valuable insights and practical experience by studying the course contents. They will learn about different datasets, data visualization techniques, and the application of deep learning models in self-driving cars.
5. Computer Vision Specialists: Professionals specializing in computer vision will find this course beneficial as it covers a wide range of topics related to computer vision for self-driving cars. They will learn about road segmentation, object detection, 3D object detection, and camera to bird's eye view transformation using UNetXST (UNet with eXtended Spatial Transformer).