Published 6/2023
Created by Fikrat Gasimov
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 76 Lectures ( 10h 25m ) | Size: 5.8 GB
Docker, Tensorflow, Pytorch, Onnx, TensorRT, model detection, model classification, model fine-tuning
What you’ll learn
1. What is Docker and How to use Docker
2. What is Kubernet and How to use with Docker
3. Nvidia SuperComputer and Cuda Programming Language
4. What are OpenCL and OpenGL and when to use ?
6. Tensorflow and Pytorch Installation, Configuration with Docker
7. DockerFile, Docker Compile and Docker Compose Debug file configuration
8. Different YOLO version, comparisons, and when to use which version of YOLO according to your problem
9. Jupyter Notebook Editor as well as Visual Studio Coding Skills
10. Visual Studio Code Setup and Docker Debugger with VS
11. what is ONNX fframework and how to use apply onnx to your custom problems
11. What is TensorRT Framework and how to use apply to your custom problems
12. Custom Detection, Classification, Segmentation problems and inference on images and videos
13. Python3 Object Oriented Programming
14. Pycuda Language programming
15. Deep Learning Problem Solving Skills on Edge Devices, and Cloud Computings
16. How to generate High Performance Inference Models , in order to get high precision, FPS detection as well as less gpu memory consumption
17. Visual Studio Code with Docker
Requirements
basic python programming knowledge
basic deep learning knowledge
Description
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, and usage of tensorflow ,pytorch, keras models with docker. In addition, they will be able to optimize and quantize/optimize deeplearning models with ONNX and TensorRT frameworks for deployment in variety of sectors such as on edge devices (nvidia jetson nano, tx2, agx, xavier), automative, robotics as well as cloud computing via aws and google platform. 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 Inference
Who this course is for
new graduates
university students
AI experts
Embedded Software Engineer
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