Text Processing Using Deep Learning

Key Takeaways

• Apply supervised and unsupervised techniques to solve core natural language processing tasks and interpret model outputs to assess performance and limitations.
• Design, implement, and optimize advanced deep neural network architectures for industry applications, with consideration for training stability and computational efficiency.
• Leverage transfer learning principles and pretrained large language models to adapt models effectively for GenAI tasks.
• Fine-tune, evaluate, and deploy large language models responsibly, while identifying risks such as bias, hallucination, security, and data leakage.
• Develop end-to-end GenAI solutions through hands-on case studies by translating real-world problem statements into executable and implementable GenAI pipelines.

Who Should Attend

• Software developers and AI engineers building language AI applications
• Data and analytics professionals moving into NLP / GenAI work
• Solution architects and technical leads evaluating language AI solutions
• Technically inclined domain professionals who want to understand and apply LLM-based systems in practice

Pre-requisites

• Able to program and debug independently using Python.
• Well-versed with Anaconda, Jupyter Notebooks, Google Colab and Github.
• Basic knowledge of machine learning concepts (at the level of Problem Solving using Pattern Recognition course offered by NUS-ISS)
• Prior exposure to data analysis or software development is recommended

What to Bring

No printed copies of course materials are issued.
Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials.

If you are bringing a laptop, please see below for the tech specs:

What Will Be Covered

• NLP fundamentals and machine learning for text
• Statistical and representation learning for text
• Deep neural network architectures for NLP
• Sequence-to-sequence models and attention
• Transformer architecture
• Transfer learning and pretrained language models
• Fine-tuning pretrained language models
• Large language models, reasoning, and alignment
• Responsible and secure use of LLMs
• End-to-end NLP and GenAI case study

Fees & Subsidies

1. All fees and subsidies are valid from January 2024, unless otherwise advised.
2. All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
3. From 1st January 2024, the GST will be increased to 9%.

Certificate

Preparing for Your Course

NUS-ISS Course Registration Terms and Conditions

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NUS-ISS and Learner’s Commitment and Responsibilities

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WIFI Access

WIFI access will be made available to participants.

Venue

NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615

Click HERE for directions to NUS-ISS

In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.

Course Confirmation

All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.

General Enquiry

Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.