Welcome!

This site is a dedicated digital portfolio built using Jupyter Book.

It is designed to give you a clear, structured, and interactive overview of my Elective 4: Deep Learning work. Each page provides in-depth documentation and examples of my skills and projects.

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What You Will Find:

• Detailed Project Narratives: The why and how behind my key work.

• Technical Demonstrations: Code, output, and visual results in an accessible format.

• Skills in Action: Practical application of my core technical competencies.

Use the table of contents to browse the complete collection of my portfolio pieces.

Lectures

• Lecture 1 - Foundational Concept of Deep Learning
• Lecture 2 - Understanding Deep Learning
• Lecture 3 - PyTorch Tensor Objects Attributes and Methods
• Lecture 4 - Main Types of Deep Learning Neural Networks
• Lecture 5 - Applications of Deep Learning
• Lecture 6 - Training CNN Model using MNIST Dataset

Exercises

• Reaction Paper: “Machine Learning: Living in the Age of AI | A WIRED Film”

Labs

• Laboratory Activty 1
• Laboratory Activty 2
• Laboratory Activity 3
• Laboratory Activity 4
• Laboratory Task 5
• Laboratory Task 6
• Lab Activity 201: Exploring Hyperparameters (Activation Functions and Optimizers)

Group Projects

• Group Activities
• Applying Temporal Fusion Transformers for Air Quality Forecasting in Metro Manila

Deep Learning Blog Post

• Adam Unchained: Breaking Free from Default Parameters

Final Project/Exam

• Automated ASL Fingerspelling Recognition for Educational Platforms Using YOLOv8 Frame Classification and Letter Sequence Reconstruction