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Technical Shortcomings of Deep Learning - Part 1

Offered By: Neuro Symbolic via YouTube

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Deep Learning Courses Artificial Intelligence Courses Machine Learning Courses Supervised Learning Courses Neural Networks Courses Transfer Learning Courses Explainable AI Courses Neuro-Symbolic AI Courses

Course Description

Overview

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Explore the technical challenges facing deep learning in this insightful video lecture. Delve into the critical appraisal of deep learning based on Gary Marcus' 2018 paper, covering key issues such as data hunger, limited transfer capacity, open-ended inference, and explainability. Gain valuable insights from Paulo Shakarian of Arizona State University as he breaks down these fundamental challenges that apply to most supervised machine learning approaches. Access accompanying slides for a comprehensive understanding of the topic and discover the implications for the future of artificial intelligence and machine learning.

Syllabus

Objectives
Deep Learning is Data Hungry
Limited Capacity for Transfer
Open-ended inference
Explainability


Taught by

Neuro Symbolic

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