patch-2
  1. ml-interviews-book
  2. contents
Name
..
images
0-about-the-answers.md
0-about-the-author.md
0-about-the-questions.md
0-acknowledgments.md
0-gaming-the-interview-process.md
0-target-audience.md
1.1-different-ml-roles.md
1.1.1-working-in-research-vs.-workingin-production.md
1.1.2-research.md
1.1.2.1-research-vs.-applied-research.md
1.1.2.2-research-scientist-vs.-research-engineer.md
1.1.3-production.md
1.1.3.1-production-cycle.md
1.1.3.2-machine-learning-engineer-vs.-software-engineer.md
1.1.3.3-machine-learning-engineer-vs.-data-scientist.md
1.1.3.4-other-technical-roles-in-ml-production.md
1.1.3.5-understanding-roles-and-titles.md
1.2-types-of-companies.md
1.2.1-applications-companies-vs.-tooling-companies.md
1.2.2-enterprise-vs.-consumer-products.md
1.2.3-startups-or-big-companies.md
2.1-understanding-the-interviewers’-mindset.md
2.1.1-what-companies-want-from-candidates.md
2.1.1.1-technical-skills.md
2.1.1.2-non-technical-skills.md
2.1.1.3-what-exactly-is-culture-fit.md
2.1.1.4-junior-vs-senior-roles.md
2.1.1.5-do-i-need-a-ph.d.-to-work-in-machine-learning.md
2.1.2-how-companies-source-candidates.md
2.1.3-what-signals-companies-look-for-in-candidates.md
2.2-interview-pipeline.md
2.2.1-common-interview-formats.md
2.2.2-alternative-interview-formats.md
2.2.3-interviews-at-big-companies-vs.-at-small-companies.md
2.2.4-interviews-for-internships-vs.-for-full-time-positions.md
2.3-types-of-questions.md
2.3.1-behavioral-questions.md
2.3.1.1-background-and-resume.md
2.3.1.2-interests.md
2.3.1.3-communication.md
2.3.1.4-personality.md
2.3.2-questions-to-ask-your-interviewers.md
2.3.3-bad-interview-questions.md
2.4-red-flags.md
2.5-timeline.md
2.6-understanding-your-odds.md
3.1-compensation-package.md
3.1.1-base-salary.md
3.1.2-equity-grants.md
3.1.3-bonuses.md
3.1.4-compensation-packages-at-different-levels.md
3.2-negotiation.md
3.2.1-compensation-expectations.md
3.3-career-progression.md
4.1-how-long-do-i-need-for-my-job-search.md
4.2-how-other-people-did-it.md
4.3-resources.md
4.3.1-courses.md
4.3.2-books-&-articles.md
4.3.3-other-resources.md
4.4-do’s-and-don’ts-for-ml-interviews.md
4.4.1-do’s.md
4.4.2-don’ts.md
5.1-algebra-and-calculus.md
5.1.1-vectors.md
5.1.2-matrices.md
5.1.3-dimensionality-reduction.md
5.1.4-calculus-and-convex-optimization.md
5.2-probability-and-statistics.md
5.2.1-probability.md
5.2.1.1-basic-concepts-to-review.md
5.2.1.2-questions.md
5.2.2-stats.md
6.1-algorithms.md
6.2-complexity-and-numerical-analysis.md
6.3-data.md
6.3.1-data-structures.md
7.1-basics.md
7.2-sampling-and-creating-training-data.md
7.3-objective-functions,-metrics,-and-evaluation.md
8.1-classical-machine-learning.md
8.1.1-overview:-basic-algorithm.md
8.1.2-questions.md
8.2-deep-learning-architectures-and-applications.md
8.2.1-natural-language-processing.md
8.2.2-computer-vision.md
8.2.3-reinforcement-learning.md
8.2.4-other.md
8.3-training-neural-networks.md
a.-for-interviewers.md
appendix.md
b.-building-your-network.md
chapter-1.-ml-jobs.md
chapter-2.-machine-learning-interview-process.md
chapter-3.-after-an-offer.md
chapter-4.-where-to-start.md
chapter-5.-math.md
chapter-6.-computer-science.md
chapter-7.-machine-learning-workflows.md
chapter-8.-machine-learning-algorithms.md
notation.md
part-i.-overview.md
part-ii.-questions.md
the-zen-of-interviews.md