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Global Tracks. Much appreciation to Jeremy and Rachel who gave me this opportunity to learn. Question : What if I have a bigger images than the model is trained with?

Question : Can we unfreeze just specific layers? Jeremy almost never finds it helpful and he thinks it is because we are using differential learning rates, and the optimizer can learn just as much as it needs to.

PDF 25 Steps to Learning 2/1 Part 2: More Advanced

The one place he found it helpful is if he is using a really big memory intensive model and he is running out of GPU, the less layers you unfreeze, the less memory and time it takes. Using differential learning rate, we are up to Intuitively speaking [ ], if the cycle length is too short, it starts going down to find a good spot, then pops out, and goes down trying to find a good spot and pops out, and never actually get to find a good spot.

Earlier on, you want it to do that because it is trying to find a spot that is smoother, but later on, you want it to do more exploring. We are introducing more and more hyper parameters having told you that there are not many. You can get away with just choosing a good learning rate, but then adding these extra tweaks helps get that extra level-up without any effort.

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In general, good starting points are:. Question: why do smoother surfaces correlate to more generalized networks? Say you have something spiky blue line. X-axis is showing how good this is at recognizing dogs vs. Something to be generalizable means that we want it to work when we give it a slightly different dataset. Slightly different dataset may have a slightly different relationship between this parameter and how cat-like vs. It may, instead look like the red line. In other words, if we end up at the blue pointy part, then it will not going to do a good job on this slightly different dataset.

Or else, if we end up on the wider blue part, it will still do a good job on the red dataset. Our model has achieved But can we make it better still? Here, Jeremy printed out the whole of these pictures. When we do the validation set, all of our inputs to our model must be square. The reason is kind of a minor technical detail, but GPU does not go very quickly if you have different dimensions for different images. It needs to be consistent so that every part of the GPU can do the same thing.

This may probably be fixable but for now that is the state of the technology we have. To make it square, we just pick out the square in the middle — as you can see below, it is understandable why this picture was classified incorrectly:. What this means is that we are going to take 4 data augmentations at random as well as the un-augmented original center-cropped. We will then calculate predictions for all these images, take the average, and make that our final prediction. To do this, all you have to do is learn.

TTA — which brings up the accuracy to Questions on augmentation approach[ ]: Why not border or padding to make it square? Typically Jeremy does not do much padding, but instead he does a little bit of zooming. There is a thing called reflection padding that works well with satellite imagery.

Generally speaking, using TTA plus data augmentation, the best thing to do is try to use as large image as possible. Also, having fixed crop locations plus random contrast, brightness, rotation changes might be better for TTA. Question: Data augmentation for non-image dataset? It seems like it would be helpful, but there are very few number of examples. In natural language processing, people tried replacing synonyms for instance, but on the whole the area is under researched and under developed. Question : Is fast. He then covered the reason why Fast. Random note: PyTorch is much more than just a deep learning library.

It actually lets us write arbitrary GPU accelerated algorithms from scratch — Pyro is a great example of what people are now doing with PyTorch outside of deep learning. The simple way to look at the result of a classification is called confusion matrix — which is used not only for deep learning but in any kind of machine learning classifier.


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It is helpful particularly if there are four or five classes you are trying to predict to see which group you are having the most trouble with. Most incorrect cats only the left two were incorrect — it displays 4 by default :. This is a little bit different to our previous dataset.

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Instead of train folder which has a separate folder for each breed of dog, it has a CSV file with the correct labels. We will read CSV file with Pandas. Pandas is what we use in Python to do structured data analysis like CSV and usually imported as pd :. Hence n is the number of images we have. Question : How many images should we use as a validation set? If you train the same model multiple times and you are getting very different validation set results, then your validation set is too small.

If the validation set is smaller than a thousand, it is hard to interpret how well you are doing. If you care about the third decimal place of accuracy and you only have a thousand things in your validation set, a single image changes the accuracy. If you care about the difference between 0. Starting training on small images for a few epochs, then switching to bigger images, and continuing training is an amazingly effective way to avoid overfitting. Question : How do we deal with unbalanced dataset? A recent paper says the best way to deal with very unbalanced dataset is to make copies of the rare cases.