# Transfer learning Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/transfer-learning
> Markdown URL: https://aitinkerers.org/technologies/transfer-learning.md
> Technology record last updated: 2026-02-23T21:56:07Z
> Generated: 2026-09-22T21:44:25Z

Repurpose pre-trained models (like ResNet or BERT) to solve new tasks with minimal data and compute.

Transfer learning bypasses the need for massive datasets by leveraging weights from models trained on massive corpuses: ImageNet for vision or Wikipedia for NLP. Instead of training 100 million parameters from scratch, you freeze the base layers and fine-tune the final classification head. This approach cuts training time by up to 90% and enables high-performance AI on niche datasets (such as identifying rare medical anomalies) where only a few hundred samples exist. Industry standards like BERT and EfficientNet rely on this logic to deliver state-of-the-art results without the massive energy costs of full-scale training.

- Official technology site: https://www.tensorflow.org/tutorials/images/transfer_learning
- Public AI Tinkerers demos and talks: 4
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Modèle CNN comme Deep Learning pour la reconnaissance des expressions faciales](https://conakry.aitinkerers.org/talks/rsvp_SsgPDZp0VSw)

Optimisation d’un modèle CNN pour la reconnaissance des expressions faciales (FER2013) » La présentation portera sur : L’architecture CNN utilisée (ResNet18 et ResNet50 en transfer learning), Les choix méthodologiques (split stratifié, gestion du déséquilibre), Les techniques de régularisation (mixup, label smoothing, dropout), Les décisions d’optimisation et les difficultés rencontrées, Une analyse des performances obtenues (Accuracy et F1 macro).

- Event context: AI Demo Day @ Kofi Annan - 25/02/26 — 2026-02-25 — Conakry
- Public talk page: https://conakry.aitinkerers.org/talks/rsvp_SsgPDZp0VSw

### [Seguridad Infantil: El Uso de Cámaras de Video e IA para el Cuidado de Niños](https://bogota.aitinkerers.org/talks/rsvp_GHOgUtTJPL0)

El proyecto "Seguridad Infantil" tiene como meta principal el desarrollo de un sistema de monitoreo inteligente que emplea cámaras de video equipadas con inteligencia artificial (IA) para salvaguardar la seguridad y el bienestar de los niños. Este sistema está diseñado para ofrecer a padres y cuidadores una herramienta efectiva que les permita supervisar a los menores, detectar situaciones de riesgo y responder de manera ágil ante posibles emergencias. El proyecto incluye varios procesos clave, tales como: Redes Neuronales Convolucionales (CNN): Estas son fundamentales para la detección y clasificación de imágenes. Se emplearán técnicas de transferencia de aprendizaje, utilizando conjuntos de datos específicos para entrenar modelos que mejoren la precisión al identificar situaciones de riesgo. Análisis de Video en Tiempo Real: La implementación de IA permitirá analizar transmisiones de video en vivo, facilitando la identificación de comportamientos peligrosos en tiempo real. Análisis Predictivo: Se aplicarán modelos de aprendizaje automático que examinarán datos históricos para anticipar comportamientos de riesgo e identificar patrones relacionados con la victimización. Estos modelos se alimentarán con información sobre interacciones reales de peligro. Modelos de Redes Neuronales Recurrentes (RNN): Se utilizarán RNN para el análisis de series temporales, lo que permitirá comprender las variaciones en el comportamiento de un niño a lo largo del tiempo y detectar señales tempranas de riesgo. Privacidad Diferencial: Se garantizará que los sistemas de IA cumplan con normativas de protección de datos, como el GDPR o el COPPA, mediante la implementación de técnicas de privacidad diferencial. Esto permitirá a las organizaciones obtener información valiosa sin poner en riesgo la privacidad de los menores.

- Event context: AI Tinkerers Bogotá - Women Edition — 2024-11-28 — Bogotá
- Public talk page: https://bogota.aitinkerers.org/talks/rsvp_GHOgUtTJPL0

### [PREMIER SPORTS](https://dubai.aitinkerers.org/talks/rsvp_9275Yfq6xKc)

Title: Predicting Football Match Outcomes with AI: My Approach to Premier Sports Description: I'm excited to share my project, PREMIER SPORTS, an AI-powered application designed to predict football match outcomes. By leveraging advanced machine learning algorithms and data analytics, I aim to provide accurate predictions that enhance game analysis for fans, coaches, and analysts alike. My approach combines historical team performance data with real-time player and team statistics to deliver more insightful and actionable information. I'm particularly interested in exploring how my model can be fine-tuned using transfer learning techniques, enabling me to adapt to changing team lineups and player performances. I'll also be showcasing some key features of my tool, including: Real-time data ingestion from various sources (e.g., sports databases, social media) Advanced feature engineering techniques for handling high-dimensional data Model selection and hyperparameter tuning Technical Depth and Focus: To provide a more comprehensive understanding of my approach, I'll be focusing on the following technical aspects: Data Handling: I employ a combination of libraries such as pandas, to carry out feature engineering, for labelling and extracting as much relevant data as possible. Transfer Learning Techniques: I leverage pre-trained language models such as Llama to capture contextual relationships within predicted match data and give insights to the application user. Live Demonstration and Code Walk-through: To enhance the audience's understanding of my approach, I'll be providing a live demonstration that includes: Interactive Data Visualization: I'll use a library like Plotly to create an interactive visualization of my data, allowing the audience to explore different scenarios and see how my model generates predictions. Code Walk-through: My demo will include a code walk-through where I explain the reasoning behind specific design choices, highlighting how AI techniques can be applied to improve the accuracy and interpretability of my model. Generative AI Integration: To further enhance the interpretability of my model, I'll be incorporating generative AI techniques: Text Generation: I'll use Llama to generate text-based outputs (e.g., player profiles, and team statistics) that can provide additional context for predictions. Clarity and Engagement To ensure clarity and engagement during the presentation: Concrete Examples: I'll use concrete examples from real-world football matches together with a web UI dashboard, to illustrate how my model can be applied in practice.

- Event context: AI Tinkerers - Dubai Meetup #3 (November) — 2024-11-10 — Dubai
- Public talk page: https://dubai.aitinkerers.org/talks/rsvp_9275Yfq6xKc

### [Identifying semiconductor manufacturer from images with convolutional nets](https://boston.aitinkerers.org/talks/rsvp_lI3z3DPEilI)

I'd like to showcase simple approaches to use convolutional nets — a fundamental building block for deep learning in computer vision — to identify the manufacturer of a semiconductor chip. Semiconductors looks fairly similar when photographed, and telling them apart can be difficult from just an image. I recently implemented a simple approach to fine-tune pre-trained convnets that solves the task quite well. I'd like to briefly go over the approach I used, how I set up experimentation etc.

- Event context: July 2024 Meetup at C10 Labs — 2024-07-22 — Boston
- Public talk page: https://boston.aitinkerers.org/talks/rsvp_lI3z3DPEilI

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