With the growth of computers, there is substantial growth in data. In today’s world, machine learning tasks often involve using large neural networks which require access to high-end GPUs to train large datasets which is expensive and takes a lot of time to train to achieve good accuracy. When there are limited resources and time, it may then pose a problem. This tutorial has a solution for handling these large datasets.
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Deep learning models, which are often deemed to be the state of the art, are specially equipped to find hidden patterns from large datasets as they learn to craft features. However, training these deep learning models is very demanding both in terms of computational resources and large training data. The deeper the model, the more are parameters to be learnt. This makes models more and more data-hungry to achieve good generalization. This begs the question what…
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Much of deep learning owes its success to the staggering amount of data used in model training. While throwing data at these deep models has shown to improve their accuracies time and time again, it comes at the great expense of data labeling. Indeed, mid-size datasets of tens of thousands of points may cost anywhere from a couple thousand USD to a couple hundred thousand USD.
Distil is a toolkit in PyTorch which provides access to different active learning algorithms. Active Learning (AL) helps in reducing labeling cost and also reduces training time and resources. AL helps in selecting only the required data and experiments show that using only 10% of data for training can reach accuracy levels close to the levels reached when using the entire dataset.
This article provides a step by step explanation of how to use DISTIL along with your existing pipelines. …