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Machine Learning for Physicists (Lecture 10): Applications in Science
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Lecture 10: Applications of deep neural networks in science and in physics
Contents: General considerations in applying neural networks to science, example applications in various domains (like chemistry and medicine) and especially in physics (e.g. statistical physics), artificial scientific discovery as a long-term goal: what would be needed?
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.
This video on the official FAU channel:
Contents: General considerations in applying neural networks to science, example applications in various domains (like chemistry and medicine) and especially in physics (e.g. statistical physics), artificial scientific discovery as a long-term goal: what would be needed?
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.
This video on the official FAU channel:
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