Deep Brain AI

To whom it may concern!

"Interviewers should not give a Data Scientist candidate a coding interview to figure out if they can really code. Instead a few questions could help them assess the candidate's coding skill even during a phone screen:

-- How do you structure your projects and code?

-- What are your coding best practice strengths and what are the worst practices you're working on?

-- If you run into a problem and there's no answer on Stack, how do you continue to research it?

-- What is your debugging process and what's the ugliest issue you've had to run down?

While the first 2 are about preventing things from going wrong, the last 2 are about todos when something does go wrong.

Answers may vary from candidates to candidates: strong coders will give substantive answers to all 4 questions, weak coders will give canned, high level answers.

Ideally, the assessment of the hiring manager should be focused on process, not syntax. He/she should focus on candidate's depth of experience, not length of experience. They should try to understand the foundational concepts & their implementations, not how much they have memorized" - Vin Vashishta

You can use LabML to organize machine learning experiments and monitor training progress and hardware usage from your phone. The current version supports PyTorch and Keras.

GitHub: https://github.com/lab-ml/labml

lab-ml/labml 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱 - lab-ml/labml