
Full Complete TensorRT Vs ONNX Course. Get Hired with Advance Unique Knowledge – By PhD Researcher AI & Robotics Scientist Fikrat Gasimov
What you’ll learn
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1. What is Docker and How to use Docker & their practical usage
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2. What is Kubernet and How to use with Docker & their practical usage
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3. Nvidia SuperComputer and Cuda Programming Language & their practical usage
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4. What are OpenCL and OpenGL and when to use & their practical usage
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6.(LAB) Tensorflow/TF2 and Pytorch Installation, Configuration with DOCKER
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7. (LAB)DockerFile, Docker Compile and Docker Compose Debug file configuration
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8. (LAB)Different YOLO version, comparisons, and when to use which version of YOLO according to your problem
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9. (LAB)Jupyter Notebook Editor as well as Visual Studio Coding Skills
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10. (LAB) Visual Studio Code Setup and Docker Debugger with VS
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11. (LAB) what is ONNX fframework and how to use apply onnx to your custom problems
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11. (LAB) What is TensorRT Framework and how to use apply to your custom problems
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12. (LAB) Custom Detection, Classification, Segmentation problems and inference on images and videos
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13. (LAB) Python3 Object Oriented Programming
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14.(LAB)Pycuda Language programming
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15. (LAB) Deep Learning Problem Solving Skills on Edge Devices, and Cloud Computings
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16. (LAB) How to generate High Performance Inference Models , in order to get high precision, FPS detection as well as less gpu memory consumption
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17. (LAB) Visual Studio Code with Docker
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18.(LAB Challenge) yolov4 onnx inference with opencv dnn
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19.(LAB Challenge) yolov5 onnx inference with opencv dnn
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20.(LAB Challenge) yolov5 onnx inference with Opencv DNN
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21.(LAB Challenge) yolov5 onnx inference with TensorRT and Pycuda
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22.(LAB) ResNet Image Classificiation with TensorRT and Pycuda
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23.(LAB) yolov5 onnx inference on Video Frames with TensorRT and Pycuda
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24. (LAB) Prepare Yourself for Python Object Oriented Programming Inference!
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25. (LAB) Python OOP Inheritance Based on YOLOV7 Object Detection
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26. Deep Theoretical Knowledge about Small Target Detection and Image Masking
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27. Deep Insight on Yolov5/Yolov6/Yolov7/Yolov8 Architectures and Practical Use Cases
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28. Deep Insight on YoloV5 P5 and P6 Models & Their Practical Usage
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29. Key Differences:Explicit vs. Implicit Batch Size
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30. (Theory) TenSorRT Optimization Profile Tutorial
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31. (Theory) Boost TensorRT Knowledge for Beginner Level Quizzies
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32. (Theory Challenge) Boost TensorRT Knowledge for Intermediate Level Quizzies
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33. Theory Challenge) Boost TensorRT Knowledge for Advance Level Quizzies
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34.(Theory Challenge) Boost Cuda Runtime for Beginner/Intermediate/Advance practical & theorytical Quizzies
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35.(Theory Challenge) Boost your OpenCV-ONNX Knowledge by doing Mixed practical & theorytical Quizzies
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36.(Deep Theoratical Knowledge) YoloV8 ONNX Model Input and Output Inference
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37.(Deep Theoratical Knowledge) YoloV8 Model usage and applied sectors.
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38.(Deep Practical Knowledge) YoloV8 ONNX Model for Detection and Segmentation
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39.(Bonus Lecture) Mastering Deep Reinforcement Learning with Advance Exercises
Can I download BOOTCAMP for TensorRT-ONNX 12+ projects and Python course?
You can download videos for offline viewing in the Android/iOS app. When course instructors enable the downloading feature for lectures of the course, then it can be downloaded for offline viewing on a desktop.Can I get a certificate after completing the course?
Yes, upon successful completion of the course, learners will get the course e-Certification from the course provider. The BOOTCAMP for TensorRT-ONNX 12+ projects and Python course certification is a proof that you completed and passed the course. You can download it, attach it to your resume, share it through social media.Are there any other coupons available for this course?
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