YoVDO

Divide and Conquer: Carving Out Symbolic Models from BlackBox for More Efficient Domain Adaptation

Offered By: Computational Genomics Summer Institute CGSI via YouTube

Tags

Machine Learning Courses Artificial Intelligence Courses Transfer Learning Courses Medical Imaging Courses Domain Adaptation Courses Fine-Tuning Courses

Course Description

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore a 32-minute lecture from the Computational Genomics Summer Institute (CGSI) 2023 on dividing and conquering symbolic models from black box systems for more efficient domain adaptation. Delve into the importance of model explanation, general design approaches, pros and cons, desirable properties, and the general idea and setup. Examine applications in medical imaging through a toy example, comparing performance, transferability, and fine-tuning. Gain insights from related research papers to enhance understanding of the topic.

Syllabus

Introduction
Why do we need model explanation
General design approaches
Pros and cons
desirable properties
general idea
general setup
Medical Imaging
Toy Example
Performance Comparison
Transferability
Fine Tuning
Performance
Conclusion


Taught by

Computational Genomics Summer Institute CGSI

Related Courses

Business Considerations for 5G with Edge, IoT, and AI
Linux Foundation via edX
FinTech for Finance and Business Leaders
ACCA via edX
AI-900: Microsoft Certified Azure AI Fundamentals
A Cloud Guru
AWS Certified Machine Learning - Specialty (LA)
A Cloud Guru
Azure AI Components and Services
A Cloud Guru