A capstone project in collaboration with Zama to develop a privacy-preserving machine learning model using PPML, FHE and Concrete ML to detect banking frauds.
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Improving code quality with linters (Pylint, Black, NbQA) #21
This issue aims to integrate linters such as Pylint, Black, or NbQA (notebook) to improve the quality, readability, and adherence to coding conventions in the project, including Jupyter notebooks. This will help detect potential errors before execution and ensure standards-compliant code.
This issue aims to integrate linters such as Pylint, Black, or NbQA (notebook) to improve the quality, readability, and adherence to coding conventions in the project, including Jupyter notebooks. This will help detect potential errors before execution and ensure standards-compliant code.