[Update Links] Learn TensorRT, ONNX with Detection,Segmentation 12 PROJECTS | Udemy

For WHOM , THIS COURSE is HIGHLY ADVISABLE:

This course is mainly considered for any candidates(students, engineers,experts) that have great motivation to learn deep learning model training and deeployment. Candidates will have deep knowledge of docker, usage of TENSORFLOW ,PYTORCH, KERAS models with DOCKER. In addition, they will be able to OPTIMIZE , QUANTIZE deeplearning models with ONNX and TensorRT frameworks for deployment in variety of sectors such as on edge devices (nvidia jetson nano, tx2, agx, xavier, qualcomm rb5, rasperry pi, particle photon/photon2), AUTOMATIVE, ROBOTICS as well as cloud computing via AWS, AZURE DEVOPS, GOOGLE CLOUD, VALOHAI, SNOWFLAKES. 

Usage of TensorRT and ONNX in Edge Devices:

Edge Devices are built-in hardware accelerator with nvidia gpu that allows to acccelare real time inference 20x Faster to achieve fast and accurate performance.

1. nvidia jetson nano, tx2, agx, xavier : jetpack 4.5/4.6 cuda accelerative libraries
2. Qualcomm rb5  together with Monoculare and Stereo Vision Camera(CSI/MPI , USB camera )
3. Particle photon/photon2  IoT in order to achieve Web API, through speech recognition systems , for Smart House
4. Robotics: Robot Operations Systems packages  for monocular and Stereo Vision Camera, in order to 3D Tranquilation ,for Human Tracking and Following, Anomaly Target and Noise Detection such as (gun noise, extremely high background  noise)
5. Rasperry Pi 3A/3B/4B gpu OpenGL compiler based

Usage of TensorRT and ONNX in Robotics Devices:

1. Overview of Nvidia Devices and Cuda compiler language
2. Overview Knowledge of OpenCL and OpenGL
3. Learning and Installation of Docker from scratch
4. Preparation of DockerFiles, Docker Compose as well as Docker Compose Debug file
5. Implementing and Python codes via both Jupyter notebook as well as Visual studio code
6. Configuration and Installation of Plugin packages in Visual Studio Code
7. Learning, Installation and Confguration of frameworks such as Tensorflow, Pytorch, Kears with docker images from scratch
8. Preprocessing and Preparation of Deep learning datasets for training and testing
9. OpenCV  DNN
10. Training, Testing and Validation of Deep Learning frameworks
11. Conversion of prebuilt models to Onnx  and Onnx Inference on images
12. Conversion of onnx model to TensorRT engine
13. TensorRT engine Inference on images and videos
14. Comparison of achieved metrices and result between TensorRT and Onnx Inference
15. Prepare Yourself for Python Object Oriented Programming Inference!
16. Deep Knowledge on Yolov5 P5 and P6 Large Models
17. Deep Knowledge on Yolov5/YoloV6 Architecture and Their Use Cases
18. Deep Theoretical and Practical Coding Skill on Research Paper of Yolov7/Yolov8 Small and Large Models
19. Boost TensorRT Knowledge for Beginner Level Quizzies
20. Boost TensorRT Knowledge for  Intermediate Level Quizzies
21. Boost TensorRT  Knowledge for Advance Level Quizzies
22. Boost Nvidia-Drivers for Beginner/Intermediate/Advance practical & theorytical Quizzies
23. Boost  Cuda Runtime for Beginner/Intermediate/Advance practical & theorytical Quizzies
24. Boost your OpenCV-ONNX Knowledge by doing Mixed  practical & theorytical Quizzies
25. ONNX beginner and Advance Pythons coding Skills for auto-tuning Yolov8 ONNX model hyperparameters and Input (Fast Image or Video Pre-Post processing) for Detection and Semantic Segmentation
26. Deep Reinforcement learning with practical example and deep python programming such as Game of Frozen Lake, Drone of Lunar Lader etc
27. Beginner, Intermediate Vs Advance Transfer Learning Custom Models
28. Beginner, Intermediate Vs Advance Object Classification
29. Beginner, Intermediate Vs Advance Object Localization and Detection
30. Beginner, Intermediate Vs Advance Image Segmentation
31. AI For Medical Treatment
32. Implement yourseld Advance Object detection and Segmentation Metrics