AI Vibe Coding for Deep Learning
About This Course
This course equips participants with practical skills to use AI vibe coding and Python to develop deep learning applications. Learners will use natural-language instructions and AI coding assistants to generate, explain, test, debug, and improve code, making neural network development more accessible without requiring every component to be written manually.
Participants will explore the foundations of deep learning, including artificial neurons, network layers, activation functions, loss functions, gradient descent, and backpropagation. They will learn how to prepare datasets, design neural network architectures, train models, evaluate results, and adjust parameters to improve performance.
The course covers practical deep learning applications such as image classification, visual recognition, text analysis, and predictive modelling. Learners will build fully connected neural networks and convolutional neural networks while using AI-assisted workflows to select suitable architectures, troubleshoot training issues, and interpret model outputs.
Through hands-on projects, participants will develop end-to-end deep learning workflows, from data preparation and model development to testing, visualisation, and deployment planning. Emphasis is placed on validating AI-generated code, reducing overfitting, maintaining data quality, comparing model performance, and applying responsible AI practices.
By the end of the course, learners will be able to use AI vibe coding with Python to build, train, evaluate, and optimise deep learning models for real-world applications. This course is suitable for beginner and intermediate learners with basic programming or data analytics knowledge.
What You'll Learn
LO1 - Setup Deep Learning frameworks
LO2 - understand and code Neural Network models for Regression
LO3 - understand and code Neural Network models for Classification
LO4 - understand and code Convolutional Neural Network models for Image Classification
LO5 - understand and use pre-trained models for transfer learning
Course Outline:
Topic 1: AI Vibe Coding and Deep Learning Environment Setup
Topic 2: Neural Networks for Regression and Predictive Modelling
Topic 3: Neural Networks for Data Classification
Topic 4: Convolutional Neural Networks for Image Classification
Topic 5: Transfer Learning and Fine-Tuning Pre-Trained Models
Entry Requirements
Knowledge and Skills
• Able to operate computer functions with minimum Computer Literacy Level 2 based on ICAS Computer Skills Assessment Framework
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience.
• Minimum 18 years old