#MachineLearning
🧐 Understanding how to build AI models is one thing. Understanding why AI models provide the results they provide is another.
🤓 Read this amazing blog 'Explaining the Explainable AI: A 2-Stage Approach', here.
🧐 Understanding how to build AI models is one thing. Understanding why AI models provide the results they provide is another.
🤓 Read this amazing blog 'Explaining the Explainable AI: A 2-Stage Approach', here.
#machinelearning
Scikit-learn team has come up with a MOOC course on "Machine learning in Python with Scikit-learn". 🤓
Github 🔗https://github.com/inria/scikit-learn-mooc
Happy learning! 😎
#supportus
~ @geekcode
Scikit-learn team has come up with a MOOC course on "Machine learning in Python with Scikit-learn". 🤓
Github 🔗https://github.com/inria/scikit-learn-mooc
Happy learning! 😎
#supportus
~ @geekcode
GitHub
GitHub - INRIA/scikit-learn-mooc: Machine learning in Python with scikit-learn MOOC
Machine learning in Python with scikit-learn MOOC. Contribute to INRIA/scikit-learn-mooc development by creating an account on GitHub.
#MachineLearning
SimSwap: An Efficient Framework For High Fidelity Face Swapping! 😯
📌GitHub repo: https://github.com/neuralchen/SimSwap
📌 Paper: https://arxiv.org/pdf/2106.06340v1.pdf
~ @geekcode
SimSwap: An Efficient Framework For High Fidelity Face Swapping! 😯
📌GitHub repo: https://github.com/neuralchen/SimSwap
📌 Paper: https://arxiv.org/pdf/2106.06340v1.pdf
~ @geekcode