An in-depth guide to tensorboard with examples in plotting loss functions, accuracy, hyperparameter search, image visualization, weight visualization as well as tensorboards visual embedding or projector.

The functions we look at from Pytorchs tensorboard are add_scalar, add_image, add_histogram, add_embedding and add_hparams.

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

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PyTorch Playlist:
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OUTLINE:
0:00 - Introduction
2:08 - Initializing SummaryWriter
2:55 - Creating a loss and accuracy plot
5:40 - Doing Hyperparameter search
16:36 - Visualizing Dataset Images and Network Weights
20:55 - Tensorboard Embedding Projector

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I was inspired to make these videos by this specialization: https://bit.ly/3SqLuA6

Image-to-Image Translation with Conditional Adversarial Networks: https://arxiv.org/abs/1611.07004

GitHub Repository:
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#PaperReview #PaperExplained

GAN Playlist:
https://youtube.com/playlist?l....ist=PLhhyoLH6IjfwIp8

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

Timestamps:
0:00 - Introduction
1:29 - Overview of paper
2:25 - Why GANs for Pix2Pix
3:16 - Loss Function
5:12 - Generator Architecture
9:24 - Discriminator Architecture
12:00 - Some training details
13:24 - Turkers to evaluate GANs
14:10 - Patch size for Discriminator
15:19 - Generator works for larger images
15:50 - More details for implementation
19:05 - Ending