Overview
Over the duration of this course, you’ll:
- Learn about computer vision foundations
- Learn about Artificial Intelligence vs Machine Learning
- Learn about the Computer Vision process
- Dive deep into real-world examples
- Understand how to build a model
- Know how to improve model performance
- Learn the Tapway labeling tools
- Understand how to choose between project types
- Become familiar with the Tapway deployment options
- Get insight into our best practices and tips
Skills Covered
You will learn how to:
- Make an informed decision about choosing a project type
- Effectively use each labeling tool
- Understand how to consider and clarify all aspects of your computer vision use case
- Create and maintain a labeling guide
- Build your own computer vision model
- Improve the performance of your trained model
- Make an informed decision about choosing a deployment option
- Deploy your trained model to the edge and configure the edge software to run with real time video streams
Prerequisites
There are no prerequisites required to attend this course.
Target Audience
- Production and/or Operation Managers
- Technical Managers
- IT Managers
- Quality Control and Quality Assurance
- Technicians
- Students and professionals in computer science, engineering, and related fields
- Anyone who is interested in learning how computers can see
Module 1: Introduction to Deep Learning / Artificial Intelligence
- What’s the difference between AI vs ML?
Module 2: Introduction to Computer Vision
- Computer vision model types i.e. Object detection, Classification, Segmentation
-Popular AI models
-Evolution of CNN and Vision Transformers - AI Computer vision process
- Examples
Module 3: Deep dive into AI Computer Vision workflow
- Dataset
-How many images do you need to train a model?
-Considerations for choosing the best images for your AI model
-Do’s and don’ts of image curation - Label datasets
-Considerations for choosing the appropriate “classes”
-Labeling guide book
-Labeling and quality checking process - Model Training
-Fast vs advanced training
-Data augmentation and transformation
-Analyze model performance
-Improve model performance
Module 4: Practical Hands-on Training Exercise for Datasets and Training Modules
Module 5: Deployment
- Make Predictions on Your Model
- Cloud Predict Endpoint Deployment
- Edge Software Deployment
Module 6: Edge AI Software Setup Process
- Camera or source setup
- Inference pipeline builder
- Middleware pipeline builder
Module 7: Practical Hands-on Training Exercise for Edge Software Deployment
- Deployment of Edge AI software to a test device
- Deployment of Edge AI software to a cloud Virtual Machine (VM) instance
- AI Inference Pipeline Builder and Configuration
Module 8: Wrap up
Exam & Certification
This course is not associated with any Certification.
Training & Certification Guide
Why train with Trainocate
Speak to a Training Consultant
All courses are HRD Claimable.
Get in touch with our team via the form or WhatsApp us on +6011-5119 6631
