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Machine Learning for Physicists (Lecture 4): Convolutional Neural Networks, Autoencoders, PCA
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Lecture 4: Convolutional Neural Networks, Autoencoders, Principal Component Analysis
Contents: A bit more on image recognition, convolutional neural networks as an efficient way to process images and other data with translational invariance, (kernels, channels, and so on), autoencoders for unsupervised learning and information compression, principal component analysis as a simple linear way to extract the main (linear) features of data sets
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: A bit more on image recognition, convolutional neural networks as an efficient way to process images and other data with translational invariance, (kernels, channels, and so on), autoencoders for unsupervised learning and information compression, principal component analysis as a simple linear way to extract the main (linear) features of data sets
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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