Machine Learning for Physicists (Lecture 5): Principal Component Analysis, t-SNE, Adam etc., ...
Lecture 5: Principal Component Analysis, t-SNE and unsupervised dimensionality reduction, Advanced Gradient Techniques, Introduction to Recurrent Neural Networks
Contents: More about the principal component analysis, unsupervised dimensionality reduction techniques for clustering and other applications (t-SNE etc.), advanced gradient descent techniques (like ‘adam’ and its siblings), first introduction to recurrent neural networks
Lecture series by Florian Marquardt: Introduction to deep learning for physicists. The whole series covers: Backpropagation, convolutional networks, autoencoders, recurrent networks, Boltzmann machines, reinforcement learning, and more.
Lectures recorded in 2019, tutorials delivered in 2020 online. Friedrich-Alexander Universität Erlangen-Nürnberg, Germany (https://www.fau.eu).
https://pad.gwdg.de/Machine_Learning_For_Physicists_2020
This video on the official FAU channel:
https://www.fau.tv/clip/id/11487
Видео Machine Learning for Physicists (Lecture 5): Principal Component Analysis, t-SNE, Adam etc., ... канала Florian Marquardt
Contents: More about the principal component analysis, unsupervised dimensionality reduction techniques for clustering and other applications (t-SNE etc.), advanced gradient descent techniques (like ‘adam’ and its siblings), first introduction to recurrent neural networks
Lecture series by Florian Marquardt: Introduction to deep learning for physicists. The whole series covers: Backpropagation, convolutional networks, autoencoders, recurrent networks, Boltzmann machines, reinforcement learning, and more.
Lectures recorded in 2019, tutorials delivered in 2020 online. Friedrich-Alexander Universität Erlangen-Nürnberg, Germany (https://www.fau.eu).
https://pad.gwdg.de/Machine_Learning_For_Physicists_2020
This video on the official FAU channel:
https://www.fau.tv/clip/id/11487
Видео Machine Learning for Physicists (Lecture 5): Principal Component Analysis, t-SNE, Adam etc., ... канала Florian Marquardt
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Lecture 4: Loss functions. Overfitting. Dropout. Adaptive Gradient Descent. Convolutional networks.
Lecture 26: Active Learning for Network Training: Uncertainty Sampling and other approaches.
Animation: Variational Autoencoder
Lecture 23: Reinforcement Learning - Policy Gradient and Q-Learning.
Lecture 14: Boltzmann Machines (General Theory).
Lecture 19: Graph Neural Networks. Attention Mechanisms (Basics).
Lecture 10: Inductive Bias. Fisher Information. Information Geometry.
Moderne Physik: "Auf der Jagd nach kosmischen Teilchen." (Prof. Anna Nelles)
Lecture 21: Transformers (and examples). Implicit Layers.
Lecture 12: Mutual Information. Learning Probability Distributions. Normalizing Flows.
Talk: Discovering feedback strategies for open quantum systems via deep reinforcement learning
Machine Learning for Physicists (Lecture 3): Training networks, Keras, Image recognition
Lecture 16: Variational Autoencoder. Generative Adversarial Networks.
Lecture 11: Natural Gradient. Kullback-Leibler Divergence. Mutual Information.
Lecture 15: Restricted Boltzmann Machines. Conditional Sampling. Variational Autoencoder.
Lecture 25: Reinforcement Learning: Continuous actions. Model-based. Monte Carlo Tree Search.
Lecture 7: Contractive Autoencoder. Shannon's Information Theory: Compression and Information.
Lecture 27: Bayesian Optimal Experimental Design. Active Learning: Gaussian Processes and Networks.
Lecture 22: Implicit Layers. Hamiltonian and Lagrangian Networks. Reinforcement Learning Overview.
Lecture 20: Attention. Differentiable Neural Computer. Transformers.