The performance of deep learning models has improved significantly on several computer vision tasks, but yet supervised learning models rely on a large number of labeled images. We know how expensive it is to get high-quality annotations, and this motivates research in other directions, including Active Learning.
In the last blog post about indoor air quality monitoring we discussed the importance and need of monitoring indoor environments for indoor air pollutants and even went into details about what are the most commonly occurring indoor air pollutants. This post will shed light on how we can use IoT to solve the problem of monitoring indoor air quality in real time. The global indoor air quality monitoring market is expected to grow from USD 2.5 billion in 2015 to USD 4.6 billion by 2022. There are many solutions available in the market today from vendors like Honeywell and lesser known startups around this problem.
Indoor air pollution is the lesser known and talked-about problem of our lives today. While we are adequately aware about the problem of air pollution outdoors, what skips our attention is the harmful air that we are exposed to in our indoor environments.
In the previous posts of the 'Clustering in ML Series', we learned about the Connectivity-Based Clustering methods in ML. In this part, we will discuss another couple of clustering methods, Distribution-Based Clustering, and Fuzzy Clustering.
In the previous posts of the 'Clustering in ML Series', we learned about the Density-Based Clustering methods in ML. In this part, we will discuss another clustering method, known as Connectivity-Based Clustering. It is also known as Hierarchical clustering.