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Indhold leveret af FAU and Prof. Dr. Andreas Maier. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af FAU and Prof. Dr. Andreas Maier eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
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Deep Learning 2018 (QHD 1920 - Video & Folien)

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Indhold leveret af FAU and Prof. Dr. Andreas Maier. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af FAU and Prof. Dr. Andreas Maier eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practices for training and evaluating neural networks visualization of networks and results common architectures, such as LeNet, Alexnet, VGG, GoogleNet recurrent neural networks (RNN, TBPTT, LSTM, GRU) deep reinforcement learning unsupervised learning (autoencoder, RBM, DBM, VAE) generative adversarial networks (GANs) weakly supervised learning applications of deep learning (segmentation, object detection, speech recognition, ...)
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13 episoder

Artwork

Deep Learning 2018 (QHD 1920 - Video & Folien)

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Arkiveret serie ("Inaktivt feed" status)

When? This feed was archived on June 19, 2022 01:28 (2y ago). Last successful fetch was on August 21, 2020 07:08 (3+ y ago)

Why? Inaktivt feed status. Vores servere kunne ikke hente et gyldigt podcast-feed i en længere periode.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage series 2432490
Indhold leveret af FAU and Prof. Dr. Andreas Maier. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af FAU and Prof. Dr. Andreas Maier eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.
Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry. This lecture introduces the core elements of neural networks and deep learning, it comprises: (multilayer) perceptron, backpropagation, fully connected neural networks loss functions and optimization strategies convolutional neural networks (CNNs) activation functions regularization strategies common practices for training and evaluating neural networks visualization of networks and results common architectures, such as LeNet, Alexnet, VGG, GoogleNet recurrent neural networks (RNN, TBPTT, LSTM, GRU) deep reinforcement learning unsupervised learning (autoencoder, RBM, DBM, VAE) generative adversarial networks (GANs) weakly supervised learning applications of deep learning (segmentation, object detection, speech recognition, ...)
  continue reading

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