Machine Learning to Predict Chaos: Echo State Networks
TREND 2020 student Chelsea Russell (Oglethorphe University) tells us about a machine learning technique called reservoir computing, which relies on echo state networks
14:40 Echo State Networks section
Podcast script and narration by Chelsea Russell
Poster displayed in video by TREND 2020 student Joseph Harvey (Hillsdale College)
Видео Machine Learning to Predict Chaos: Echo State Networks канала TREND REU
14:40 Echo State Networks section
Podcast script and narration by Chelsea Russell
Poster displayed in video by TREND 2020 student Joseph Harvey (Hillsdale College)
Видео Machine Learning to Predict Chaos: Echo State Networks канала TREND REU
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
![TREND REU 2018 - Reservoir Computing and Chaos (Tatiana Davidson Bajandas)](https://i.ytimg.com/vi/ksvr0JfC-aI/default.jpg)
![JuliaSim: Accelerated Simulation of Stiff HVAC Systems with Continuous-Time Echo State Networks](https://i.ytimg.com/vi/ZaYinbYWkYE/default.jpg)
![Embedding and Approximation Theorems for Echo State Networks](https://i.ytimg.com/vi/7dYh1wK_3zE/default.jpg)
![Illustrated Guide to Recurrent Neural Networks: Understanding the Intuition](https://i.ytimg.com/vi/LHXXI4-IEns/default.jpg)
![SUM2021 - Photonic reservoir computing for high-speed neuromorphic computing applications - A.Lugnan](https://i.ytimg.com/vi/08Tft_rmY5A/default.jpg)
![Chaos Theory | The Butterfly Effect (ft. Jabrils)](https://i.ytimg.com/vi/JrJNBlS6Okc/default.jpg)
![Tatiana Davidson Bajandas - Reservoir Computing Explained (TREND REU)](https://i.ytimg.com/vi/R8V0RI4GSZ8/default.jpg)
![Chaos Theory 🦋 Analog Lorenz Attractor](https://i.ytimg.com/vi/DFKm0K5O7ak/default.jpg)
![Neural Networks for Dynamical Systems](https://i.ytimg.com/vi/JfeB_n4zsRM/default.jpg)
![AI Learns to Park - Deep Reinforcement Learning](https://i.ytimg.com/vi/VMp6pq6_QjI/default.jpg)
![The Mathematics of Machine Learning](https://i.ytimg.com/vi/Rt6beTKDtqY/default.jpg)
![Reservoir computing (or training recurrent neural networks)](https://i.ytimg.com/vi/lmbhfntqohY/default.jpg)
![Hello World - Machine Learning Recipes #1](https://i.ytimg.com/vi/cKxRvEZd3Mw/default.jpg)
![Pathak - Model free replication of chaotic attractors from data: A reservoir computing approach](https://i.ytimg.com/vi/Cfb2SMsCMb0/default.jpg)
![Random Neural Networks — Erol Gelenbe / Serious Science](https://i.ytimg.com/vi/ztZklp7NC5o/default.jpg)
![Is it Possible to Predict Randomness? The Double Pendulum Experiment](https://i.ytimg.com/vi/4xViPStT5II/default.jpg)
![Machine Learning vs Deep Learning : quelle différence ?](https://i.ytimg.com/vi/esiKN7k2IBI/default.jpg)
![Learning by Observing via Inverse Reinforcement Learning](https://i.ytimg.com/vi/5Cfd5btfR3g/default.jpg)
![Reservoir Computing with Superconducting Circuits](https://i.ytimg.com/vi/1bWjyQ1326g/default.jpg)
![RSS Applied Probability Section: Rough path theory in machine learning](https://i.ytimg.com/vi/2lSH26EQzac/default.jpg)