Advancing Continual Learning for Robotics Applications

Advancing Continual Learning for Robotics Applications - Printable Version

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Advancing Continual Learning for Robotics Applications - AD-TEAM - 03-08-2025

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Advancing Continual Learning for Robotics Applications
Published 2/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 43m | Size: 1.2 GB

Continual Learning, Incremental Learning, Brain-Inspired lifelong learning Algorithms, Robotics Applications

What you'll learn
Learn deep learning algorithms through simple, math-focused explanations.
Explore continual learning with in-depth analysis of algorithms, scenarios, and strategies.
Focus on real-world applications, especially in robotics.
Practice with Python tutorials and quick quizzes to test your knowledge.

Requirements
No prerequisites required, but basic algebra and Python knowledge are a plus.

Description
This course takes you on a journey through the core principles of deep learning (DL), starting with the essential mathematical foundations that power these algorithms. From there, we dive into the limitations of traditional DL when faced with non-stationary or non-iid (independent and identically distributed) data. You'll then be introduced to the exciting world of continual learning (CL) - also known as lifelong or incremental learning - exploring real-world scenarios and a variety of strategies used in cutting-edge CL research. We'll deep dive into some of the most impactful and robust CL algorithms, followed by a captivating exploration of a novel CL application in controlling a soft robotic arm. With engaging videos, interactive presentations, detailed notes, hands-on tutorials, and quizzes after every section to evaluate the learned concepts, this course is designed to help you master the topic and their real-world applications. Whether you're new to CL or looking to expand your knowledge in machine learning, this lecture series will help you gain practical insights and learn how to apply these algorithms to solve complex problems. By the end of the course, you'll have a strong foundation in CL and its potential to transform industries like robotics, autonomous systems, real-time systems and beyond.

Who this course is for
Ideal for ML researchers, roboticists, PhD students, or anyone looking to expand their expertise.

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