Загрузка страницы

Moderne Physik: "Auf der Jagd nach kosmischen Teilchen." (Prof. Anna Nelles)

Live Aufzeichung aus der "Moderne Physik" Vortragsreihe des Department für Physik an der Friedrich-Alexander-Universität Erlangen-Nürnberg

"Über Gemeinsamkeiten von Feuerzeugen, Motoren und Neutrinos. Auf der Jagd nach kosmischen Teilchen."

Vortrag vom 30.11.2021, Prof. Anna Nelles

https://moderne-physik.de

Видео Moderne Physik: "Auf der Jagd nach kosmischen Teilchen." (Prof. Anna Nelles) канала Florian Marquardt
Показать
Комментарии отсутствуют
Введите заголовок:

Введите адрес ссылки:

Введите адрес видео с YouTube:

Зарегистрируйтесь или войдите с
Информация о видео
1 декабря 2021 г. 12:48:39
00:45:12
Другие видео канала
Lecture 4: Loss functions. Overfitting. Dropout. Adaptive Gradient Descent. Convolutional networks.Lecture 4: Loss functions. Overfitting. Dropout. Adaptive Gradient Descent. Convolutional networks.Lecture 26: Active Learning for Network Training: Uncertainty Sampling and other approaches.Lecture 26: Active Learning for Network Training: Uncertainty Sampling and other approaches.Animation: Variational AutoencoderAnimation: Variational AutoencoderLecture 23: Reinforcement Learning - Policy Gradient and Q-Learning.Lecture 23: Reinforcement Learning - Policy Gradient and Q-Learning.Lecture 14: Boltzmann Machines (General Theory).Lecture 14: Boltzmann Machines (General Theory).Lecture 19: Graph Neural Networks. Attention Mechanisms (Basics).Lecture 19: Graph Neural Networks. Attention Mechanisms (Basics).Lecture 10: Inductive Bias. Fisher Information. Information Geometry.Lecture 10: Inductive Bias. Fisher Information. Information Geometry.Lecture 21: Transformers (and examples). Implicit Layers.Lecture 21: Transformers (and examples). Implicit Layers.Lecture 12: Mutual Information. Learning Probability Distributions. Normalizing Flows.Lecture 12: Mutual Information. Learning Probability Distributions. Normalizing Flows.Talk: Discovering feedback strategies for open quantum systems via deep reinforcement learningTalk: Discovering feedback strategies for open quantum systems via deep reinforcement learningMachine Learning for Physicists (Lecture 3): Training networks, Keras, Image recognitionMachine Learning for Physicists (Lecture 3): Training networks, Keras, Image recognitionLecture 16: Variational Autoencoder. Generative Adversarial Networks.Lecture 16: Variational Autoencoder. Generative Adversarial Networks.Lecture 11: Natural Gradient. Kullback-Leibler Divergence. Mutual Information.Lecture 11: Natural Gradient. Kullback-Leibler Divergence. Mutual Information.Lecture 15: Restricted Boltzmann Machines. Conditional Sampling. Variational Autoencoder.Lecture 15: Restricted Boltzmann Machines. Conditional Sampling. Variational Autoencoder.Machine Learning for Physicists (Lecture 5): Principal Component Analysis, t-SNE, Adam etc., ...Machine Learning for Physicists (Lecture 5): Principal Component Analysis, t-SNE, Adam etc., ...Lecture 25: Reinforcement Learning: Continuous actions. Model-based. Monte Carlo Tree Search.Lecture 25: Reinforcement Learning: Continuous actions. Model-based. Monte Carlo Tree Search.Lecture 7: Contractive Autoencoder. Shannon's Information Theory: Compression and Information.Lecture 7: Contractive Autoencoder. Shannon's Information Theory: Compression and Information.Lecture 27:  Bayesian Optimal Experimental Design. Active Learning: Gaussian Processes and Networks.Lecture 27: Bayesian Optimal Experimental Design. Active Learning: Gaussian Processes and Networks.Lecture 22: Implicit Layers. Hamiltonian and Lagrangian Networks. Reinforcement Learning Overview.Lecture 22: Implicit Layers. Hamiltonian and Lagrangian Networks. Reinforcement Learning Overview.Lecture 20: Attention. Differentiable Neural Computer. Transformers.Lecture 20: Attention. Differentiable Neural Computer. Transformers.
Яндекс.Метрика