Semi-supervised Learning explained

Key Takeaways

[Music] in this video we'll be discussing the concept of semi-supervised learning semi-supervised learning kind of takes a middle ground between supervised learning and unsupervised learning as a quick refresher recall from previous videos that supervised learning is the learning that occurs during training of an artificial neural network when the data in our training set is labeled unsupervised learning on the other hand is the learning that occurs when the data in our training set is not labeled so now onto semi-supervised learning semi-supervised learning uses a combination of supervised and unsupervised learning techniques and that's because in a scenario where we'd make use of semi-supervised learning we'd have a combination of both labeled and unlabeled data let's expand on this idea with an example say we have access to a large unlabeled data set that we'd like to train a model on and that manually labeling all of the state ourselves is just not practical well we could go through and manually label some portion of this large data set ourselves and use that portion to train our model and this is fine in fact this is how a lot of data use for neural networks becomes labeled but you know if we have access to large amounts of data and we've only labeled some small portion of the data then what a waste it would be to just leave all the other unlabeled data on the table I mean after all we know the more data we have to train a model on the better and more robust our model will be so what can we do to make use of the remaining unlabeled data in our data set well one thing we can do is implement a technique that falls under the category of semi-supervised learning called pseudo labeling this is how pseudo labeling works so as just mentioned we've already labeled some portion of our data set now we're going to use this label data as the training set for our model we're then going to train our model just as we would with any other labelled data set okay and then just through the regular training process we get our model performing pretty well so everything we've done up to this point has been just regular old supervised learning in practice now here's where the unsupervised learning piece comes into play after we've trained our model on the labeled portion of the data set we then use our model to predict on the remaining unlabeled portion of data we then take these predictions and label each piece of unlabeled data with the individual outputs that were predicted for them this process of labeling the unlabeled data with the output that was predicted by our neural network is the very essence of pseudo labeling now after labeling the unlabeled data through this pseudo labeling process we then train our model on the full data set which is now comprised of both the data that was actually truly labeled along with the data that was pseudo labeled through the use of pseudo labeling were able to train on a vastly larger data set we're also able to train on data that otherwise may have potentially taken many tedious hours of human labor to manually label the data as you can imagine sometimes the cost of acquiring or generating a fully label data set is just too high or the pure act of generating all the labels itself is just not feasible so through this process we can see how this approach makes use of both supervised learning with the labeled data and unsupervised learning with the unlabeled data which together give us the practice of semi-supervised learning so hopefully now you have an understanding of what semi-supervised learning is and how you may apply it and practice through the use of pseudo labeling and I hope you found this video helpful if you did please like the video subscribe suggest and comment and thanks for watching [Music]