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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 HouseRobotics: 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 basedUsage of TensorRT and ONNX in Robotics Devices:Overview of Nvidia Devices and Cuda compiler languageOverview Knowledge of OpenCL and OpenGL Learning and Installation of Docker from scratchPreparation of DockerFiles, Docker Compose as well as Docker Compose Debug fileImplementing and Python codes via both Jupyter notebook as well as Visual studio codeConfiguration and Installation of Plugin packages in Visual Studio CodeLearning, Installation and Confguration of frameworks such as Tensorflow, Pytorch, Kears with docker images from scratchPreprocessing and Preparation of Deep learning datasets for training and testingOpenCV DNN Training, Testing and Validation of Deep Learning frameworksConversion of prebuilt models to Onnx and Onnx Inference on imagesConversion of onnx model to TensorRT engine TensorRT engine Inference on images and videosComparison of achieved metrices and result between TensorRT and Onnx InferencePrepare Yourself for Python Object Oriented Programming Inference!Deep Knowledge on Yolov5 P5 and P6 Large ModelsDeep Knowledge on Yolov5/YoloV6 Architecture and Their Use CasesDeep Theoretical and Practical Coding Skill on Research Paper of Yolov7/Yolov8 Small and Large ModelsBoost TensorRT Knowledge for Beginner Level QuizziesBoost TensorRT Knowledge for Intermediate Level QuizziesBoost TensorRT Knowledge for Advance Level QuizziesBoost Nvidia-Drivers for Beginner/Intermediate/Advance practical & theorytical QuizziesBoost Cuda Runtime for Beginner/Intermediate/Advance practical & theorytical QuizziesBoost your OpenCV-ONNX Knowledge by doing Mixed practical & theorytical QuizziesONNX 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 SegmentationDeep Reinforcement learning with practical example and deep python programming such as Game of Frozen Lake, Drone of Lunar Lader etcBeginner, Intermediate Vs Advance Transfer Learning Custom ModelsBeginner, Intermediate Vs Advance Object ClassificationBeginner, Intermediate Vs Advance Object Localization and DetectionBeginner, Intermediate Vs Advance Image SegmentationAI For Medical TreatmentImplement yourseld Advance Object detection and Segmentation Metrics

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