Click to learn more about author Paolo Tamagnini. Welcome to the sixth episode of our Guided Labeling Blog Series. In the last episode, we made an analogy with a number of “friends” labeling “movies” with three different outcomes: “good movie” (?), “not seen movie” ( – ), “bad movie” (?). We have seen how we can train a […]
Guided Labeling Episode 5: Blending Knowledge with Weak Supervision
Click to learn more about author Paolo Tamagnini. Welcome to the fifth episode of our Guided Labeling Blog Series.In the last four episodes, we introduced Active Learning and a practical example with body mass index data, which shows how to perform active learning sampling via the technique “exploration vs exploitation”. This technique employs label density and model uncertainty […]
Guided Labeling Episode 4: From Exploration to Exploitation
Click to learn more about author Paolo Tamagnini. One of the key challenges in using supervised machine learning for real world use cases is that most algorithms and models require a sample of data that is large enough to represent the actual reality your model needs to learn. These data need to be labeled. These […]
Guided Labeling Episode 3: Model Uncertainty
Click to learn more about author Paolo Tamagnini. In this series, we’ve been exploring the topic of guided labeling by looking at active learning and label density. In the first episode, we introduced the topic of active learning and active learning sampling and moved on to look at label density in the second article. Here […]
Guided Labeling Episode 2: Label Density
Click to learn more about author Paolo Tamagnini. The Guided Labeling series of blog posts began by looking at when labeling is needed — i.e., in the field of machine learning when most algorithms and models require huge amounts of data with quite a few specific requirements. These large masses of data need to be […]
Guided Labeling Episode 1: An Introduction to Active Learning
Click to learn more about author Paolo Tamagnini. One of the key challenges of utilizing supervised machine learning for real-world use cases is that most algorithms and models require lots of data with quite a few specific requirements. First of all, you need to have a sample of data that is large enough to represent […]