Clustering in machine learning has a variety of applications, but how do you know which algorithm is best suited to your data? Here's how to amplify your data insights with comparison metrics, including the F-measure.
Two heads are better than one. This proverb describes the concept behind ensemble methods in machine learning. Let's examine why ensembles dominate ML competitions and what makes them so powerful.
Harnessing the potential of machine learning for computer vision is not a new concept but recent advances and the availability of new tools and datasets have made it more accessible to developers. In this article, Toptal Software Developer Teimur Gasanov demonstrates how you can create an app capable of identifying handwritten digits in under 30 minutes, including the API and UI.
Retailers often face supply and demand issues that cause them to miss out on potential sales or tie up a lot of money in overstocked products. In this article, Toptal Data Scientist Ahmed Khaled explains how retailers can boost revenues and cut costs with sales forecasts backed by artificial intelligence.
Working with non-numerical data can be challenging, even for seasoned data scientists. To make good use of such data, it needs to be transformed. But how? In this article, Toptal Data Scientist Yaroslav Kopotilov will introduce you to embeddings and demonstrate how they can be used to visualize complex data and make it usable.
TensorFlow is one of the leading tools for training deep learning models. Outside that space, it may seem intimidating and unnecessary, but it has many creative uses—like producing highly effective adversarial input for black-box AI systems.
Pre-trained models are making waves in the deep learning world. Using massive pre-training datasets, these NLP models bring previously unheard-of feats of AI within the reach of app developers.
For those working with AI, the future is certainly exciting. At the same time, there is a general sense that AI suffers from one pesky flaw: AI in its current state can be unpredictably unreliable.
IMDb ratings have genre bias: For example, dramas tend to score higher. Removing common feature bias and keeping unique characteristics, it's possible to create a new, refined score based on IMDb information.
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