interviews.ai
high-school-guide-to-machine-learning
| interviews.ai | high-school-guide-to-machine-learning | |
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| 12 | 1 | |
| 4,437 | 971 | |
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| 0.0 | 2.6 | |
| over 2 years ago | 4 months ago | |
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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
high-school-guide-to-machine-learning
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[Discussion] Will machine learning eventually make it into high school math curriculum
Mathematicians and AI experts are increasingly using machine learning to solve previously intractable math problems. Getting a foundation in ML at a young age will expedite this trend.
What are some alternatives?
machine-learning-roadmap - A roadmap connecting many of the most important concepts in machine learning, how to learn them and what tools to use to perform them.
best_AI_papers_2021 - A curated list of the latest breakthroughs in AI (in 2021) by release date with a clear video explanation, link to a more in-depth article, and code.
audio-ai-timeline - A timeline of the latest AI models for audio generation, starting in 2023!
best_AI_papers_2022 - A curated list of the latest breakthroughs in AI (in 2022) by release date with a clear video explanation, link to a more in-depth article, and code.
Awesome-pytorch-list - A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.
datascience - Curated list of Python resources for data science.
Data-science-best-resources - Carefully curated resource links for data science in one place
Computer-Science-Resources - A list of resources in different fields of Computer Science
500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code - 500 AI Machine learning Deep learning Computer vision NLP Projects with code