Perceptron - AI: Det Store valg: Episode 4: Fra Fra Ada Lovelace til techbros episode artwork

EPISODE · Sep 12, 2023 · 48 MIN

Perceptron - AI: Det Store valg: Episode 4: Fra Fra Ada Lovelace til techbros

from Perceptron · host Mark Sinclair Fleeton

Det her er anden del af to, hvor vi taler med Signe Zambach, Adjunkt på CBS og Ph.d. i Kunstig Intelligens. Denne gang fortæller hun bla. om kvinder i udviklingen af AI og hvor vigtigt det er, at kvinder er en del af udviklingen. Og så tager vi hul på snakken om fremtiden med AI.Hvis du er en organisation eller virksomhed, der kan se en pointe i at samarbejde om at udbrede kendskabet til AI til almindelige mennesker, så smid mig en mail på [email protected]

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Perceptron - AI: Det Store valg: Episode 4: Fra Fra Ada Lovelace til techbros

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Deep Learning - Plain Version Course ID:1384 Prof. Dr. Andreas Maier   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 un Self development Perceptron Group Deep Learning 2018 (QHD 1920 - Video & Folien) Prof. Dr. Andreas Maier 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, ...) Deep Learning 2018 (Audio) Prof. Dr. Andreas Maier 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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This episode is 48 minutes long.

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This episode was published on September 12, 2023.

What is this episode about?

Det her er anden del af to, hvor vi taler med Signe Zambach, Adjunkt på CBS og Ph.d. i Kunstig Intelligens. Denne gang fortæller hun bla. om kvinder i udviklingen af AI og hvor vigtigt det er, at kvinder er en del af udviklingen. Og så tager vi hul...

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