📅 2025-04-04 — Session: Explored Autoencoders for Dimensionality Reduction
🕒 16:45–19:30
🏷️ Labels: Autoencoders, Dimensionality Reduction, Machine Learning, Deep Learning, Luciana Ferrer
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore the use of autoencoders for dimensionality reduction in demographic datasets and compare it with Luciana Ferrer’s work in machine learning applied to speech processing.
Key Activities
- Reviewed protocols for gastric tube replacement by nurses in Brazil, focusing on institutional guidelines and COFEN regulations.
- Conducted a comparative analysis of autoencoders and Luciana Ferrer’s research, highlighting differences in application and focus areas.
- Reflected on Luciana Ferrer’s expertise in machine learning, particularly in signal classification and natural language processing, and its potential insights into dimensionality reduction.
- Reviewed articles on the application of autoencoders in demographic data, identifying key references for further exploration.
- Compiled a list of relevant authors with expertise in autoencoders and related deep learning topics.
Achievements
- Clarified the regulatory framework for gastric tube replacement by nurses in Brazil.
- Gained insights into the application of autoencoders for dimensionality reduction and identified potential research directions.
- Identified key authors and articles for further reading on autoencoders and deep learning.
Pending Tasks
- Further explore the identified articles and authors to deepen understanding of autoencoders in demographic data processing.
- Consider potential collaborations or consultations with experts like Luciana Ferrer to enhance research depth.