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The intuition behind an ingenious algorithm

Here’s a function: f(x). It’s expensive to calculate, not necessarily an analytic expression, and you don’t know its derivative.

Your task: find the global minima.

This is, for sure, a difficult task, one more difficult than other optimization problems within machine learning. …


Creative techniques to make complex models smaller

With the advent of convolutional neural networks and transformers to handle complex image recognition and natural language processing tasks, deep learning models have skyrocketed in size.

Although the increase in size is usually associated with an increase in predictive power, this supersizing comes with undesirable costs.

  • Longer training time. Models…


Rethinking the GAN without competition

Generative Adversarial Networks (GANs) are an important development in deep learning; its formulation inspired a new generation of model ensembles that interact with each other in ways to produce incredible results.

Competition is in the blood of GAN design — “adversarial” is in its name. Two models, the discriminator and…

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Humans domain knowledge ceases to be useful

Machine learning has often been described as the study of “algorithms that create algorithms”. To a certain extent, this is true — machine learning finds the best model to fit the data. It is the process by which we attain the algorithm that can give predictions on the data.



Deep learning can’t beat human solutions — yet

The Travelling Salesman Problem was formulated in 1930, and is a classical computer science problem for optimization. It’s a simple problem:

Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city exactly once and returns to the…


Revolutionizing traditional Transfer Learning, k-Means, and the CNN

Quantum computing is a buzz-word that’s been thrown around quite a bit. Unfortunately, despite its virality in pop culture and quasi-scientific Internet communities, its capabilities are still quite limited.

As a very new field, quantum computing presents a complete paradigm shift to the traditional model of classical computing. Classical bits…

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Learning from corrupted data

Oh no! You find out that your data is corrupted — there are enough instances of training data being attached to the incorrect label for it to be a significant problem. What should you do?

If you want to be a radical optimist, you could think of this data corruption…

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The Machine Learning Experimentation Tradeoff: A Framework

If you’re a company, you’re continually seeking how to gain more profit. If a company is seeking to expand or change its current business (in both big or small ways), a common solution is experimentation.

Companies can experiment if a change works out or not; if a change does seem…

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A technical exploration into a controversial question

Machine learning has begun to pervade all aspects of life — even those protected by anti-discrimination law. It’s being used in hiring, credit, criminal justice, advertising, education, and more.

What makes this a particularly difficult problem, though, is that machine learning algorithms seem to be more fluid and intangible. Part…

Source: Pixabay.

The Undercurrent of Ongoing Research

2021 is here, and deep learning is as active as ever; research in the field is speeding up exponentially. There are obviously many more deep learning advancements that are fascinating and exciting. …

Andre Ye

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