Deep Learning - Plain Version Course ID:1384

About Deep Learning - Plain Version Course ID:1384

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, ...) The accompanying exercises will provide a deeper understanding of the workings and architecture of neural networks.

Publisher

Prof. Dr.-Ing. Andreas Maier

Category

education

Language

en

Explicit

No

First episode

26 May 2020

Latest episode

9 Jul 2020

Latest episodes

130 episodes in the feed.

1. 

   9 Jul 2020

   63 - Deep Learning - Known Operator Learning Part 2

2. 

   9 Jul 2020

   62 - Deep Learning - Known Operator Learning Part 1

3. 

   9 Jul 2020

   64 - Deep Learning - Known Operator Learning Part 3

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

Apple Podcasts (US)

5.0 / 5

1 ratings

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