Spotlight on Ghana's Rising AI Talent: Trestle Academy GH Interns' Innovative Solutions.

I recently facilitated Trestle Academy Ghana 's first internship cohort. As part of the 12-week intensive program, I guided these promising interns as they developed AI-powered solutions addressing real-world challenges in areas like healthcare, multimedia, and natural language processing.

Their creativity and technical capabilities left me thoroughly impressed. From deep learning models for facial deblurring to music hit predictions, their capstone projects highlight the vast potential of AI and talent that exists within Ghana.

AI for Simplifying Skincare Routines:

Elias Elikem Ifeanyi Dzobo developed "Clear," an ML-Powered Skincare Product Recommendation System that suggests products matched to users' skin types and preferences, a clever way to simplify skincare routines. Using dimensionality reduction and similarity analysis, his method intelligently matches users to suitable products. Explore his project

Next Word Prediction with Neural Networks:

Samuel Kwame Dassi designed a context-enhanced neural network architecture for intelligent next-word prediction, overcoming challenges like ambiguity and domain adaptation. His techniques achieved over 86% accuracy. Explore his project

Deblur-AI for Enhancing Facial Image Quality:

Osman Tanko built "Deblur AI" to address blurriness in facial images using deep learning. He trained a 5-layer UNet architecture on a 600+ image dataset. His model showed promising results in adjusting blurred pixels to produce clearer, more detailed facial images. Check out his work

Predicting Heart Disease Risk:

Franklin Kpodo developed a logistic regression model using the landmark Framingham Heart Study dataset to estimate an individual’s 10-year risk of heart disease, enabling targeted early interventions. His interface generates personalized risk assessments. Check it out

Song Hit Prediction and Music Recommendation:

Nissi Semanyoh built a system to forecast song popularity using analytics. He also generated music recommendations based on song attributes like tempo. His work aims to decode trends in the ever-changing music industry. Explore his project

Deep Learning for Brain Tumor Classification:

Derrick Sarpong employed deep learning on MRI scans to accurately classify brain tumor types. Comparing Inception and VGG-16 models, he achieved over 95% accuracy, paving the way for faster diagnosis and treatment decisions. See it here

Real-Time Face Tracking Model:

Benjamin Obeng Gyamerah created a real-time facial tracking model with computer vision techniques, which could have applications in security systems and photography. His deep learning approach accounts for diverse real-world conditions like lighting and expressions. See his work

Breast Cancer Classification Using CNNs:

Joshua Ampofo Yentumi created a convolutional neural network to detect breast cancer cases from mammogram images. His transfer learning approach with VGG19 obtained remarkable performance, aiding early screening. Explore his project

Music Genre Classification with Deep Learning:

Fifonsi Donald developed a system to categorize music into granular subgenres using neural networks. By breaking down broad categories like pop and classical, his model better captures the richness of musical diversity. View his work

While the interns faced obstacles like limited data and computing power, their perseverance and learning mindsets led them to noteworthy outcomes within a short period. I see great potential in this emerging talent pipeline for Ghana's AI ecosystem. With the right guidance and development opportunities, they could undoubtedly become global tech leaders and innovators.