I hope you’re all doing well. I recently came across an article that provides a list of deep-learning interview questions and answers. I found it quite helpful, as I’m preparing for a deep learning interview myself. However, there are a few questions in the article that I’m having trouble understanding and implementing.
Specifically, I’m struggling with the following questions:
Question XYZ: [Provide a brief description of the question]
I’m having difficulty understanding the concept behind this question and how to approach it.
If possible, could someone please provide a step-by-step explanation or share some code examples?
Question ABC: [Describe the question]
I’m unable to grasp the intuition behind this question and how to solve it.
If anyone has any insights or code snippets related to this question, I would greatly appreciate it.
If anyone has experience with deep learning or has encountered similar questions before, I would love to hear your thoughts. Any guidance, explanations, or code examples would be immensely helpful in improving my understanding.
I learned Deep learning mainly from fast.ai 5 years ago and went to many interviews and conducted several ML interviews myself this year. My general advice would be to talk manly on the projects that you have done personally. Doing a fast.ai course gives an opportunity to run the code yourself, to experiment and overcome the mistakes in particular field that is most interesting to you. If you are going to be a DL engineer in field of computer vision, it would be good to do some hobby project based on course lectures, simply running notebooks on your own images and playing with parameters.
I skimmed the provided link and found some of the questions outdated and some of them seems to be unpractical (for example “more powerful - a two layer neural network without any activation function or a two layer decision tree?”)
I would recommend reading Practical Deep Learning for Coders - The book book and watching Practical DL course, it will give most answers and questions. The book has questions in the end of every chapter for better practice.