Lab: Tuning Deep Learning Networks
- Analytics & Tech
- Artificial Intelligence
This programme is conducted on-campus.
2 days
Weekdays (9am - 6pm)
Singapore Management University
Who Should Attend
- AI and Machine Learning Engineers looking to build Gen AI applications
- AI Scientists
- Data Analysts
- Software Engineers
- IT Professionals and Team Leads
Participants are required to have a functional laptop with the following specifications:
- CPU: Must be at least Intel Core i3.
- GPU: Must have an integrated graphics card.
- RAM: Must be at least 4GB.
No prior experience is required. Participants are expected to be proficient in computer usage and possess fundamental logical thinking or numeracy skills. A proactive approach to learning through practical exercises is essential.
Overview
These skills are increasingly in demand as companies seek to tailor AI systems for competitive advantage - whether in building domain-specific chatbots, streamlining compliance workflows, or enhancing productivity through customised automation. Participants will learn techniques such as prompt engineering, parameter-efficient fine-tuning (PEFT), and reinforcement learning from human feedback (RLHF).
The module also covers Deep Learning architectures and their role in pushing the boundaries of model scalability, efficiency, and adaptability. Practical sessions allow learners to experiment with tuning LLMs for tasks like content generation and sentiment analysis. By the end of this module, participants will have the skills to customise and optimise LLMs for targeted applications, ensuring practical and flexible solutions.
Learning Objectives
- LLM Fine-tuning Techniques: Understand foundational and advanced techniques for fine-tuning large language models (LLMs)
- Prompt Engineering: Learn prompt engineering for efficient interaction with LLMs
- PEFT & RLHF: Explore parameter-efficient fine-tuning (PEFT), full fine-tuning, and reinforcement learning from human feedback (RLHF)
- Practical Fine-tuning: Fine-tune a deep learning model, from data preparation and loading to evaluation and analysis, drawing insights from real-world implementations and case studies
Topic/Structure
- LLM Fine-tuning Techniques: Understand foundational and advanced techniques for fine-tuning large language models (LLMs)
- Prompt Engineering: Learn prompt engineering for efficient interaction with LLMs
- PEFT & RLHF: Explore parameter-efficient fine-tuning (PEFT), full fine-tuning, and reinforcement learning from human feedback (RLHF)
- Practical Fine-tuning: Fine-tune a deep learning model, from data preparation and loading to evaluation and analysis, drawing insights from real-world implementations and case studies
Assessment
- MCQ
- Written Assignment
As part of the requirement for SkillsFuture Singapore, there will be an assessment conducted at the end of every module. The mode of assessment, which is up to the trainer’s discretion, may be an online quiz, a presentation or based on classroom exercises.
Participants are required to attain a minimum of 75% attendance and pass the associated assessment in order to receive a digital Certificate of Completion issued by Singapore Management University.
Calculate Programme Fee
Fee Table
| EMPLOYER-SPONSORED | |||
|
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
|
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$719.40 (After SSG Funding 70%) |
$279.40 (After SSG Funding 70% |
$719.40 (After SSG Funding 70%) |
|
Singapore Citizen ≥ 40 years old |
$279.40 (After SSG Funding 70% |
$279.40 (After SSG Funding 70% |
$279.40 (After SSG Funding 70% |
|
International Participant |
$2,398 (No Funding) |
$2,398 (No Funding) |
$2,398 (No Funding) |
All prices include 9% GST
Please note that the programme fees are subject to change without prior notice.
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
SkillsFuture Credit
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual).Workfare Skills Support Scheme
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Employer Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees are eligible for ETSS funding only if their company's SME status is approved prior to the course commencement date. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Employers who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
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