English | Size: 5.4 GB
Genre: eLearning
Full Complete TensorRT Vs ONNX Course. Get Hired with Advance Unique Knowledge
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
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.
nvidia jetson nano, tx2, agx, xavier : jetpack 4.5/4.6 cuda accelerative libraries
Qualcomm rb5 together with Monoculare and Stereo Vision Camera(CSI/MPI , USB camera )
Particle photon/photon2 IoT in order to achieve Web API, through speech recognition systems , for Smart House
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)
Rasperry Pi 3A/3B/4B gpu OpenGL compiler based
Usage of TensorRT and ONNX in Robotics Devices:
Overview of Nvidia Devices and Cuda compiler language
Overview Knowledge of OpenCL and OpenGL
Learning and Installation of Docker from scratch
Preparation of DockerFiles, Docker Compose as well as Docker Compose Debug file
Implementing and Python codes via both Jupyter notebook as well as Visual studio code
Configuration and Installation of Plugin packages in Visual Studio Code
Learning, Installation and Confguration of frameworks such as Tensorflow, Pytorch, Kears with docker images from scratch
Preprocessing and Preparation of Deep learning datasets for training and testing
OpenCV DNN
Training, Testing and Validation of Deep Learning frameworks
Conversion of prebuilt models to Onnx and Onnx Inference on images
Conversion of onnx model to TensorRT engine
TensorRT engine Inference on images and videos
Comparison of achieved metrices and result between TensorRT and Onnx Inference
Prepare Yourself for Python Object Oriented Programming Inference!
Deep Knowledge on Yolov5 P5 and P6 Large Models
Deep Knowledge on Yolov5/YoloV6 Architecture and Their Use Cases
Deep Theoretical and Practical Coding Skill on Research Paper of Yolov7/Yolov8 Small and Large Models
Boost TensorRT Knowledge for Beginner Level Quizzies
Boost TensorRT Knowledge for Intermediate Level Quizzies
Boost TensorRT Knowledge for Advance Level Quizzies
Boost Nvidia-Drivers for Beginner/Intermediate/Advance practical & theorytical Quizzies
Boost Cuda Runtime for Beginner/Intermediate/Advance practical & theorytical Quizzies
Boost your OpenCV-ONNX Knowledge by doing Mixed practical & theorytical Quizzies
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
Deep Reinforcement learning with practical example and deep python programming such as Game of Frozen Lake, Drone of Lunar Lader etc
Beginner, Intermediate Vs Advance Transfer Learning Custom Models
Beginner, Intermediate Vs Advance Object Classification
Beginner, Intermediate Vs Advance Object Localization and Detection
Beginner, Intermediate Vs Advance Image Segmentation
Who this course is for:
new graduates
university students
AI experts
Embedded Software Engineer
Robotics Engineer
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