Ian Beauregard
Translator
Montreal, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolIan Beauregard is a bilingual translator-turned-software engineer with 14 years of professional experience, blending deep linguistic precision with practical coding skills gained at Qwasar Silicon Valley and through contributions to prominent ML education repos like handson-ml. He leverages rapid learning, meticulous attention to detail, and clear communication to improve machine learning notebooks, test suites (JUnit4), and automation projects across web and research contexts. Based in Montreal, he pairs legal translation work at SOQUIJ with ongoing software practice, uniquely able to bridge domain expertise and reproducible code. Outside work he hikes, cycles, reads, and practices Vipassana, a discipline that underpins his focused, iterative approach to both translations and software craft.
13 years of coding experience
2 years of employment as a software developer
Baccalauréat, Traduction, Baccalauréat, Traduction at Université de MontréalSoftware Engineering, Software Engineering at Qwasar Silicon Valley
French, English
Github Skills (18)
notebook10
python10
scikit10
testing10
machine-learning10
java10
javas10
keras10
junit10
deep-learning10
tensorflow10
scikit-learn10
jupyter-notebook10
nlp10
test-automation10
Programming languages (7)
Github contributions (5)
ageron/handson-ml2
Aug 2020 - Oct 2020
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Role in this project:
Data Scientist
Data ScientistContributions:6 reviews, 19 commits, 10 PRs in 2 months
Contributions summary:Ian primarily contributed to the machine learning and deep learning aspects of the project. Their work involved refining code within the `03_classification.ipynb` notebook, including parameterizing functions, improving number matching with regular expressions, and removing redundant code. Further contributions include removing deprecated methods, adjusting epoch calculations, and correcting typos across multiple notebooks. The user also updated an `Embedding` layer and installed the `transformers` library.
pythondata-sciencedeep-learningjupyter-notebookfundamentals
ageron/handson-ml3
Aug 2020 - Oct 2020
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Role in this project:ML Engineer
ML EngineerContributions:19 commits in 2 months
Contributions summary:Ian primarily contributed to the implementation and refinement of machine-learning-related code within the repository. They made several modifications to the `03_classification.ipynb` notebook, including parameter adjustments and the correction of regular expressions. Additionally, the user addressed deprecated methods and updated the calculation of epochs in the `11_training_deep_neural_networks.ipynb` notebook, suggesting a focus on improving model training and efficiency. Further contributions include removing unnecessary code and correcting typos across multiple notebooks.
pythonmxnetdata-sciencedeep-learningjupyter-notebook
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