Emil Björnson explains the basics of supervised deep learning and two useful applications of it in the physical layer of communication systems.

If you want to learn more, you can read "Two Applications of Deep Learning in the Physical Layer of Communication Systems" by Emil Björnson and Pontus Giselsson (https://arxiv.org/pdf/2001.03350)

In Application 1, the following paper is used as an example:

Trinh Van Chien, Emil Björnson, Erik G. Larsson, “Sum Spectral Efficiency Maximization in Massive MIMO Systems: Benefits from Deep Learning,” IEEE International Conference on Communications (ICC), 2019. https://arxiv.org/pdf/1903.08163.pdf

In Application 2, the following paper is used as an example:

Özlem Tugfe Demir, Emil Björnson, “Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning,” IEEE Open Journal of the Communications Society, 2020. https://arxiv.org/pdf/1911.07316.pdf

Credits
Research Paper: https://arxiv.org/pdf/1409.0473.pdf
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With the MVTec Deep Learning Tool it’s possible to train a deep-learning-based classification model from scratch. First, we import images of your application – using the folder structure, the Deep Learning Tool can assign labels automatically. We can then easily review our data by filtering the images according to these labels. After that, we train the model using transfer learning and pretrained networks provided by MVTec. Lastly, we can review the trained model in detail, using a confusion matrix, heatmaps and more. In this video, version 0.5 of the Deep Learning Tool is used. The exported trained model including the associated dictionary, which contains the preprocessing parameters, can be imported into HDevelop and MERLIC and then used for inference.

In this video, version 0.5 of the Deep Learning Tool is used.

Get more information at https://www.mvtec.com/products/deep-learning-tool/

GPT-3 has demonstrated remarkable results in human-like text generation for a wide range of contexts. Check out the video to understand this transformer-based model under the hood.