Last December, as we were looking ahead towards 2022, we wished our entire community a year full of learning, discovery, and calmer times. The world didn’t quite deliver on that last item, but we can…
It is common for data practitioners to use batch learning to learn from data. Batch learning is the training of ML models in batch. An ML pipeline with batch learning typically includes:
Color segmentation is a technique used in computer vision to identify and distinguish different objects or regions in an image based on their colors. Clustering algorithms can automatically group…
Calculating the median of an array of data is pretty straightforward in any programming language, even in Excel, where a built-in or third-party median function can be used directly. However, in…
The visualization of quantitative data through charts and graphs has the purpose of making the data easier to understand and to derive valuable insights from it. Pie charts, however, tend to do the…
How Graphcore researchers developed BESS (Balanced Entity Sampling and Sharing)
Consumer-grade cameras and lenses are cheap and ubiquitous. Unfortunately, unlike their industrial counterparts, they were not designed to be used as tools for precise measurements in computer vision…
Simpson’s paradox is a well-known statistical paradox. Like all paradoxes (by definition), even if we know the answer, it doesn’t seem intuitive. In the case of Simpson’s Paradox, the Machine…
Review of a recent work of Bytedance the parent company of Tiktok that highlights a recommendation engine that leverages online training, embeddings, hashes
One of the common tasks you often need to perform with Pandas DataFrames is that of manipulating date and time. Depending on how the date and time values are originally encoded in the dataset, you…
