Core discussion on the projects I have done what was the approach how we evaluated the results.
Learning Engineer Interview Questions
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How much familiar are you with open-sourcing?
Q: Tell me about your past projects.
Kaggle submission for a public competition. Technical questions were about my existing experience and projects I worked in.
How Decision Tree works? Explain how CNN works etc and on Image processing.
Floy is a medical AI company on the mission to maximize human healthspan. Our first AI product helps radiologists to improve the diagnostic accuracy for lumbar spine examinations. Diagnostic errors are frighteningly common and statistically affect everyone of us in our lifetime. Radiologists have a 26.1% error rate of clinically relevant findings and 38% of these errors can be prevented through the collaboration of radiologists and AI. It is unacceptable that existing technology is not adopted in radiology. It is time to change that! Our goal is to find out how you approach problems, structure ML projects and obtain tangible results under time constraint. Code quality isless important in this exploratory challenge (production code is a different story). Build a minimal working pipeline with the framework (Tensorflow, PyTorch, etc) of your choice. 1) Structure your ML pipeline. Use different cells with comments to explain you approach. 2) Develop a working pipeline. It does not have to be perfect. Focus on getting things done. 3) Answer three qualitative questions in simple words.
Floy is a medical AI company on the mission to maximize human healthspan. Our first AI product helps radiologists to improve the diagnostic accuracy for lumbar spine examinations. Diagnostic errors are frighteningly common and statistically affect everyone of us in our lifetime. Radiologists have a 26.1% error rate of clinically relevant findings and 38% of these errors can be prevented through the collaboration of radiologists and AI. It is unacceptable that existing technology is not adopted in radiology. It is time to change that! Our goal is to find out how you approach problems, structure ML projects and obtain tangible results under time constraint. Code quality isless important in this exploratory challenge (production code is a different story). Build a minimal working pipeline with the framework (Tensorflow, PyTorch, etc) of your choice. 1) Structure your ML pipeline. Use different cells with comments to explain you approach. 2) Develop a working pipeline. It does not have to be perfect. Focus on getting things done. 3) Answer three qualitative questions in simple words.
Details about the projects that I have done.
Basic Machine learning algorithms
Tell me about how transformers work. name a few functions of tensorflow.
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