Click to learn more about author Paolo Tamagnini. Welcome to the seventh episode of our Guided Labeling Blog Series. In the last six episodes, we have covered active learning and weak supervision theory. Today, we would like to present a practical example of implementing weak supervision via guided analytics based on a Workflow. The other […]
Guided Labeling Episode 6: Comparing Active Learning with Weak Supervision
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 […]
AI and Machine Learning Trends to Watch in 2023
This article highlights 10 of the biggest trends triggered by technological advancements in artificial intelligence (AI) and machine learning (ML). These trends have collectively revolutionized the way businesses approach everything from education and economics to the environment. The broad AI and machine learning trends include the provisioning of cloud platforms for data activities – accelerating the use […]
An Introduction to Reinforcement Learning
In this blog post, I’d like to introduce some basic concepts of reinforcement learning, some important terminology, and a simple use case where I create a game playing AI in my company’s analytics platform. After reading this, I hope you’ll have a better understanding of the usefulness of reinforcement learning, as well as some key […]
An Introduction to Integrated Deployment
Click to learn more about author Paolo Tamagnini. Welcome our integrated deployment blog series, where we focus on solving the challenges around productionizing Data Science. Topics will include: Resolving the challenges of deploying models Building guided analytics applications that create not only a model but a complete model process, using our component approach to AutoML […]