One of the challenges of machine learning is obtaining large enough volumes of well labelled data. An approach to mitigate the effort required for labelling data sets is active learning, in which outliers are identified and labelled by domain experts. In this episode Tivadar Danka describes how he built modAL to bring active learning to bioinformatics. He is using it for doing human in the loop training of models to detect cell phenotypes with massive unlabelled datasets. He explains how the library works, how he designed it to be modular for a broad set of use cases, and how you can use it for training models of your own.
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- Your host as usual is Tobias Macey and today I’m interviewing Tivadar Danka about modAL, a modular active learning framework for Python3
- How did you get introduced to Python?
- What is active learning?
- How does it differ from other approaches to machine learning?
- What is modAL and what was your motivation for starting the project?
- For someone who is using modAL, what does a typical workflow look like to train their models?
- How do you avoid oversampling and causing the human in the loop to become overwhelmed with labeling requirements?
- What are the most challenging aspects of building and using modAL?
- What do you have planned for the future of modAL?
Keep In Touch
- Uri Alon: An Introduction to Systems Biology – Design Principles of Biological Circuits, book and online lectures
- modAL homepage
- modAL on GitHub
- modAL paper
- Active Learning
- Supervised Learning
- Unsupervised Learning
- Active Feature-Value Acquisition
- Jupyter Notebooks
- Bayesian Optimization