What is semi-supervised learning?

Every machine learning algorithm needs data to learn from. But even with tons of data in the world, including texts, images, time-series, and more, only a small fraction is actually labeled, whether algorithmically or by hand.

Most of the time, we need labeled data to do supervised machine learning. I particular, we use it to predict the label of each data point with the model. Since the data tells us what the label should be, we can calculate the difference between the prediction and the label, and then minimize that difference.

As you might know, another category of algorithms called…

An Introduction to Transformers and Sequence-to-Sequence Learning for Machine Learning

New deep learning models are introduced at an increasing rate and sometimes it’s hard to keep track of all the novelties. That said, one particular neural network model has proven to be especially effective for common natural language processing tasks. The model is called a Transformer and it makes use of several methods and mechanisms that I’ll introduce here. The papers I refer to in the post offer a more detailed and quantitative description.

Part 1: Sequence to Sequence Learning and Attention

The paper ‘Attention Is All You Need’ describes transformers and what is called a sequence-to-sequence architecture. Sequence-to-Sequence (or Seq2Seq) is a neural net that transforms a…

Everyone who’s done a Deep Learning project knows how long it takes to train a single model not to mention optimizing it.

As a current student, I don’t have access to any GPUs which sometimes leads to frustration because I can spend most of my time waiting for the model to see all the training batches.

As you might have heard, GPUs can considerably decrease training time when used for Deep Learning projects. Why is that? And what are GPUs? In a nutshell, a GPU is a graphics processing unit that is now integrated in most laptops to efficiently manipulate…


Machine Learning Researcher & Data Scientist

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