📅 2025-04-04 — Session: Exploration of Autoencoders in Dimensionality Reduction
🕒 16:45–19:30
🏷️ Labels: Autoencoders, Dimensionality Reduction, Machine Learning, Deep Learning, NLP
📂 Project: Dev
⭐ Priority: MEDIUM
Session Goal
The session aimed to explore the use of autoencoders for dimensionality reduction, particularly in demographic datasets, and to compare this approach with the work of Luciana Ferrer in machine learning applied to speech processing.
Key Activities
- Discussed the protocol for gastric tube replacement by nurses in Brazil, emphasizing the importance of following institutional protocols and medical prescriptions.
- Compared the use of autoencoders for dimensionality reduction with Luciana Ferrer’s research in machine learning, focusing on speech processing.
- Reflected on Luciana Ferrer’s expertise in machine learning and how it might offer insights into the application of autoencoders for dimensionality reduction.
- Reviewed articles on the application of autoencoders in demographic datasets, providing references for further exploration.
- Identified authors with experience in autoencoders, deep learning, and NLP, highlighting their relevance and expertise.
Achievements
- Gained insights into the application of autoencoders for dimensionality reduction in demographic datasets.
- Established a comparison framework between autoencoders and Luciana Ferrer’s work in machine learning.
- Compiled a list of relevant authors and articles for further study.
Pending Tasks
- Further exploration of the identified articles and authors to deepen understanding of autoencoders’ application in various domains.