Dynamical Systems & machine Learning

Inspiring Talk on Machine Learning and Dynamical Systems! 🌟

Our heartfelt gratitude to Professor Zung Nguyen for the engaging talk on the relationship between Machine Learning and Dynamical Systems. The session was truly enlightening, and we learned so much about the basis of Dynamical Systems and its applications in the realm of Machine Learning.

During the talk, Professor Zung explained the crucial differences between Markovian and non-Markovian systems, shedding light on the significance of understanding their properties in various applications. The concept of stochastic equilibria was particularly intriguing and has sparked a deeper curiosity for further exploration.

Some of the notable highlights included:

1. Residual Networks: Understanding how defining the next step based on derivatives contributes to the robustness of these networks.

2. Equilibria, Convergence, and Stability in Gradient Descent: The realization that we cannot expect absolute minimum because in fact the noise also creates energy.

3. JPEG Image Compression: Unraveling the connection between Dynamical Systems and the wavelet compression technique used in the JPEG format for images.

4. Augmentation and Normalization in ML: Gaining deeper insights into the reasons why these techniques are crucial for enhancing the performance and generalization of machine learning models, concepts that have their logic in Dynamical Systems.

5. Early Detection of Skin Cancer: The eye-opening revelation that Cancer can be associated with Chaos, leading to higher entropy, and the potential implications for early detection algorithms.

For those who missed this exceptional talk, it is highly recommend checking it out on our YouTube channel. It's an opportunity to grasp the profound connections between Dynamical Systems and Machine Learning, which have the potential to revolutionize various industries and drive innovation.

Watch the video on YouTube: https://lnkd.in/gawDx5yk

Take our quiz: https://lnkd.in/gT2G49MN

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