awesome-ai-residency
interviews.ai
| awesome-ai-residency | interviews.ai | |
|---|---|---|
| 7 | 12 | |
| 2,944 | 4,437 | |
| - | - | |
| 5.5 | 0.0 | |
| 4 months ago | over 2 years ago | |
| - | - |
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awesome-ai-residency
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ML Research New Grad
For bachelors, AI residency programs may be a good (though competitive) choice. https://github.com/dangkhoasdc/awesome-ai-residency
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GaTech MSCS - it's crap
MSCS students are not expected to do independent research at GT. They are expected to assist Ph.D. students. This reflects poorly when applying for graduate school. Therefore, you **must** go for a pre-doc program instead of GT. List - https://github.com/dangkhoasdc/awesome-ai-residency
- Wrong fit for quant?
- GitHub - List of AI Residency Programs
- [D] What makes you an extremely competitive applicant for a top-tier US AI/ML master program?
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[D] Internship after ML phd?
Usually in order to be an intern at a big company, they would ask for some proof that you are still a student (but I would check it with the company). Maybe a one year AI residency would be more suitable in your case (https://github.com/dangkhoasdc/awesome-ai-residency)
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How do I transition to AI research? (I have an MS in Physics)
There are specific programs for people like you: AI residencies. These are intended as fast-track programs to get people from other fields up to speed in AI research. I have no personal experience with them though, and would expect the application process to be extremely competitive. Essentially all of the large AI players have such programs: Google, Microsoft, OpenAI, NVidia, Intel, IBM, Facebook... just google (or duckduckgo) "AI residency + [company name]". They all have different entry requirements / preferred qualifications. Check out, e.g., this guidance. Here is another residency link list and here are some pointers on how to prepare for an application.
interviews.ai
- Deep Learning Interviews
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Ask HN: Leet code/CTCI equivalent for Data science/ML roles
scientists" - those interviews focus a lot of SQL, product metrics, A/B testing etc. You can also do SQL problems on leetcode for those types of positions.
2. Deep learning interviews book for ML positions - https://github.com/BoltzmannEntropy/interviews.ai - it's a bit too deep and advanced for most interviews though so don't be intimidated if you can't cover everything. Don't read this book if you're applying for a product DS position (and vice versa). You can also replace this with an ML theory book of your choice if you like.
3. Still leetcode and CTCI because they often come up for ML positions anyway.
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what to study for MLE interviews? Is it leetcode all the way?
Regarding how to study, my suggestion is to solve problems with sample datasets. A couple of books that might come in handy. 1. https://github.com/BoltzmannEntropy/interviews.ai - I like this because there are problems and solutions in there. 2. https://huyenchip.com/ml-interviews-book/
- Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI.
- GitHub - BoltzmannEntropy/interviews.ai: Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI
- Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI
- Deep Learning Interviews book: Hundreds of fully solved job interview questions
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