In this video we go through a bit more in depth into custom datasets and implement more advanced functions for dealing with text. Specifically we're looking at a image captioning dataset (Flickr8k data set) with an image and a corresponding caption text that describes what's going on in the image. I think the general principles from this video can be utilized to any project you're working with when dealing with text data be it either translation, question answering, sentiment analysis etc. I also recommend taking a look at my Torchtext which can also be quite helpful and simplify the data loading process.

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Flickr8k Dataset used in the video:
https://www.kaggle.com/dataset..../e1cd22253a9b23b0737

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

Github repository:
https://github.com/aladdinpers....son/Machine-Learning

OUTLINE:
0:00 - Introduction
2:05 - Overview of what we're going to do
4:05 - Imports
5:20 - Setup of Pytorch Dataset for loading Flickr
11:50 - Setup of Vocabulary and Numericalization
22:19 - Creating Collate for Padding of Batch
25:20 - Function for getting data loader
29:15 - Running code & fixing couple of errors
33:09 - Ending

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