Learning Notebook - David Rostcheck
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This talk will discuss the challenges and trade-offs in creating an open source machine learning library. Andreas Mueller has been involved in the scikit-learn machine learning library for about five years. During this time, scikit-learn has become a popular machine learning library, used across applications in academia and industry. This talk will discuss some design decisions that we imagine lead to its success, as well as highlight continuing challenges. In particular, we will discuss the trade-off of following current trends and developments in machine learning, the growing demand for easy-to-use data science solutions, and the maintainability and stability of the project. While the integration of scikit-learn into the growing data science ecosystem within Python is one of its strengths, the close coupling with numpy, and the evolution of pandas come with challenges not faced in systems that are build for data science from the ground up.
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