BOOTCAMP for TensorRT-ONNX 12+ projects and Python

BOOTCAMP for TensorRT-ONNX 12+ projects and Python Course includes 18 hrs video content and enrolled by 500+ students and received a 4.4 average review out of 5. Now, you will get 90%OFF on the original price of the course and discount price differs from country to country, and the course provider offers 30-days money-back guarantee! If you are not satisfied in any way, you’ll get your money back.

Full Complete TensorRT Vs ONNX Course. Get Hired with Advance Unique Knowledge – By PhD Researcher AI & Robotics Scientist Fikrat Gasimov

What you’ll learn

  • 1. What is Docker and How to use Docker & their practical usage
  • 2. What is Kubernet and How to use with Docker & their practical usage
  • 3. Nvidia SuperComputer and Cuda Programming Language & their practical usage
  • 4. What are OpenCL and OpenGL and when to use & their practical usage
  • 6.(LAB) Tensorflow/TF2 and Pytorch Installation, Configuration with DOCKER
  • 7. (LAB)DockerFile, Docker Compile and Docker Compose Debug file configuration
  • 8. (LAB)Different YOLO version, comparisons, and when to use which version of YOLO according to your problem
  • 9. (LAB)Jupyter Notebook Editor as well as Visual Studio Coding Skills
  • 10. (LAB) Visual Studio Code Setup and Docker Debugger with VS
  • 11. (LAB) what is ONNX fframework and how to use apply onnx to your custom problems
  • 11. (LAB) What is TensorRT Framework and how to use apply to your custom problems
  • 12. (LAB) Custom Detection, Classification, Segmentation problems and inference on images and videos
  • 13. (LAB) Python3 Object Oriented Programming
  • 14.(LAB)Pycuda Language programming
  • 15. (LAB) Deep Learning Problem Solving Skills on Edge Devices, and Cloud Computings
  • 16. (LAB) How to generate High Performance Inference Models , in order to get high precision, FPS detection as well as less gpu memory consumption
  • 17. (LAB) Visual Studio Code with Docker
  • 18.(LAB Challenge) yolov4 onnx inference with opencv dnn
  • 19.(LAB Challenge) yolov5 onnx inference with opencv dnn
  • 20.(LAB Challenge) yolov5 onnx inference with Opencv DNN
  • 21.(LAB Challenge) yolov5 onnx inference with TensorRT and Pycuda
  • 22.(LAB) ResNet Image Classificiation with TensorRT and Pycuda
  • 23.(LAB) yolov5 onnx inference on Video Frames with TensorRT and Pycuda
  • 24. (LAB) Prepare Yourself for Python Object Oriented Programming Inference!
  • 25. (LAB) Python OOP Inheritance Based on YOLOV7 Object Detection
  • 26. Deep Theoretical Knowledge about Small Target Detection and Image Masking
  • 27. Deep Insight on Yolov5/Yolov6/Yolov7/Yolov8 Architectures and Practical Use Cases
  • 28. Deep Insight on YoloV5 P5 and P6 Models & Their Practical Usage
  • 29. Key Differences:Explicit vs. Implicit Batch Size
  • 30. (Theory) TenSorRT Optimization Profile Tutorial
  • 31. (Theory) Boost TensorRT Knowledge for Beginner Level Quizzies
  • 32. (Theory Challenge) Boost TensorRT Knowledge for  Intermediate Level Quizzies
  • 33. Theory Challenge) Boost TensorRT  Knowledge for Advance Level Quizzies
  • 34.(Theory Challenge) Boost  Cuda Runtime for Beginner/Intermediate/Advance practical & theorytical Quizzies
  • 35.(Theory Challenge) Boost your OpenCV-ONNX Knowledge by doing Mixed  practical & theorytical Quizzies
  • 36.(Deep Theoratical Knowledge) YoloV8 ONNX Model Input and Output Inference
  • 37.(Deep Theoratical Knowledge) YoloV8 Model usage and applied sectors.
  • 38.(Deep Practical Knowledge) YoloV8 ONNX Model for Detection and Segmentation
  • 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.
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