Computer Vision is a transformative business technology, leveraging Artificial Intelligence to automate visual interpretation, enhance decision-making, and create innovative products and services. From optimizing quality assurance and enabling autonomous systems to powering next-generation customer interfaces, deep learning has become a cornerstone of competitive advantage in the visual data landscape.
This executive course is engineered to equip professionals with commercially applicable deep learning expertise, translating neural network theory into scalable, real-world AI vision solutions that drive operational efficiency and strategic innovation.
Participants will gain a strategic understanding of Convolutional Neural Networks (CNNs), master industry-standard techniques for image data engineering and augmentation, and learn to implement advanced architectures for critical business applications—including automated inspection, visual analytics, and object recognition. The program integrates essential considerations for model deployment and the responsible implementation of vision AI within an enterprise context.
Learning Objectives
Upon completion, participants will be equipped to:
• Articulate the strategic value of deep learning for enterprise computer vision initiatives.
• Architect and explain Convolutional Neural Network (CNN) models for business applications.
• Engineer and optimize image data pipelines for model performance and scalability.
• Develop, train, and fine-tune deep learning models to solve specific operational challenges.
• Evaluate model robustness, mitigate risks such as overfitting, and quantify business impact.
• Integrate vision AI solutions into real-world products, services, and workflows.
Learning Outcomes
Participants will demonstrate competency to:
• Develop and implement production-ready CNN models using industry frameworks.
• Design and execute professional image pre-processing and augmentation pipelines.
• Manage the end-to-end model lifecycle, from training and validation to performance assessment.
• Leverage transfer learning to accelerate development and improve solution efficacy.
• Interpret model outputs and key metrics to guide business decisions and iterations.
• Navigate the deployment architecture and ethical implications of vision AI systems.
Course Code: TGS-2025061174
Course Duration: 3 days
Course Fee: $1650.00
| TYPE | Singapore Citizens and
Singapore Permanent Residents |
Employer-sponsored and self-sponsored Singapore Citizens aged 40 years old and above | SME-sponsored
Local employees (i.e Singapore Citizens and Singapore Permanent Residents) |
| SkillsFuture
Funding (Baseline) |
SkillsFuture
Mid-career Enhanced Subsidy |
SkillsFuture
Enhanced Training Support For SMEs |
|
| Course Fee | $1650.00 | $1650.00 | $1650.00 |
| SkillsFuture Funding | $825.00 | $1155.00 | $1155.00 |
| Total Nett Fee | $825.00 | $495.00 | $495.00 |
| GST (9% of Course fee) | $148.50 | $148.50 | $148.50 |
| Total Fee Payable to | $973.50 | $643.50 | $643.50 |
Price indicated are the payable amount with GST, must be made before the commencement of the course.
-
Payment by Cash/Paynow/SkillsFuture Credit (for eligible Singaporeans), cheque or Giro Bank Transfers (for companies).
-
Refund of course fees is applicable upon absence/withdrawal with a valid reason (e.g. production of mc)
*For more information on Course fee funding, please visit this link: https://www.myskillsfuture.gov.sg/content/portal/en/career-resources/career-resources/education-career-personal-development/skillsfuture-funding-changes.html
Absentee Payroll Funding Support is applicable for Employer sponsored SGs or PRs; standardised at $4.50/hr.
For more information go to: https://www.enterprisejobskills.gov.sg/content/upgrade-skills/course-fee-and-absentee-payroll-funding.html
DAY 1: Deep Learning & Computer Vision Foundations (8 Hours)
Topics Covered
- Introduction to computer vision and AI vision applications
- Review of neural networks and deep learning concepts
- Image representation and pixel data
- Introduction to Convolutional Neural Networks (CNNs)
- Convolution, pooling, and activation functions
- Building a basic CNN model
Hands-on Labs
- Image data loading and visualization
- Implementing a simple CNN for image classification
Key Skills Developed
- CNN fundamentals
- Image data handling
DAY 2: Advanced CNNs, Transfer Learning & Model Optimization (8 Hours)
Topics Covered
- Deep CNN architectures (VGG, ResNet – conceptual)
- Transfer learning concepts
- Fine-tuning pretrained models
- Image augmentation techniques
- Overfitting and regularization
- Model evaluation metrics
Hands-on Labs
- Transfer learning using pretrained models
- Improving model performance through augmentation and tuning
Key Skills Developed
- Advanced CNN usage
- Model optimization techniques
DAY 3: Applied Vision Use Cases & Deployment Readiness (8 Hours)
Topics Covered
- Object detection concepts
- Image segmentation overview
- Real-world computer vision use cases
- Model deployment concepts for vision systems
- Performance, scalability, and ethics in vision AI
- End-to-end vision AI workflow
Hands-on Labs
- Vision-based mini project (classification / detection)
- Model evaluation and result interpretation
- Deployment simulation and best practices
Key Skills Developed
- Applied computer vision problem-solving
- End-to-end AI workflow understanding
Assessment & Evaluation
|
Assessment Component |
Description |
|
Knowledge Checks |
Deep learning & vision concepts |
|
Hands-on Exercises |
CNN and transfer learning labs |
|
Mini Project |
Vision-based AI application |
|
Final Review |
Model performance & insights |
Target Audience
- AI/ML engineers and practitioners
• Data scientists and analysts
• Software developers
• Research and innovation teams
• Students and early professionals in AI
Career & Productivity Impact
Relevant Roles
• Computer Vision Engineer (Entry-level)
• AI / ML Engineer
• Deep Learning Engineer
• AI Application Developer
Productivity Gains
• Faster development of vision-based AI solutions
• Improved automation and accuracy
• Reduced manual visual inspection
• Enhanced AI-driven decision support
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