更新于 2018年2月28日 机器学习
wx:   网页版 2018-02-28 07:19
经验总结 入门 深度学习 算法 Andrew Ng Brain Advice John Hopkins Python Will Edit 博客 强化学习 数据科学 速查卡 统计
「AI研究公司面试准备指南」转自:爱可可-爱生活Hello r/MachineLearning!I am underway with an interview for an AI research company. I’m pooling all the resources I’ve found on how to tackle the interview, as well as asking for more. What I’ve found a lot of are blog posts and video lectures. Principally, I’m trying to find good practice question & answer style posts in these subjects, the more topic specific, the better. I thought I would also share the resources I have already found to motivate visibility of the post and help people in my position.From my research, I’ve found four main categories to study:Statistics & ProbabilityOther Relevant MathematicsProgrammingConcepts for quiz-like questionsPracticals for interview coding sessionsMachine LearningI know this is a popular topic on here, so I’ll start with the discussions I found on reddit and other forums. Most of these aren’t particularly useful in general, and I will post any links inside them further on down the post. I’ve kept it to the last two years, since things move pretty quickly in data science:Crash Course Materials (reddit)OpenAI Advice (reddit)Google Brain Advice (reddit)DeepMind Advice (reddit)Other post about deepmindI did not find a huge amount of useful material in the above posts. I did find blog posts were a good way to form an overall strategy:Blog Posts:Crushed it: Landing a data science jobStuff I’ve Messed Up While InterviewingData Science InterviewsHow to Prepare for a Machine Learning InterviewData Science Interview Questions with Answers (discussed)How to Ace Data Science Interviews: StatisticsCommon Probability Distributions: The Data Scientist’s Crib SheetLots of these posts recommended textbooks and coursera courses. I feel like these are useful if you are starting from zero or have lots of time:Courses & Textbooks:Andrew Ng’s Machine Learning Course (Coursera)John Hopkins’s Biostatistics Bootcamp (Coursera)A First Course in Probability, Ross, 8th edition (PDF textbook)Has self-test with answers as wellStatistical Inference, Casella & Berger, 2nd edition (PDF textbook)Lots of people like “cheat-sheets.” I think they are a good study aide, but can be too information dense to use as primary material. I will call this “reference material.”Reference Material:Great overview of probability distributions (blog post)Python for Data Science : Keras & NumpyML Algorithm Flowchart / Cheat SheetIf you’re like me and are around one week out from your interview, I find question sheets as the ultimate study material, bonus points if they have answers. This guides my study and informs to what level I should know things, otherwise the amount of resource and material is overwhelming. I am really looking for more of these, please comment with some if you know where to find them, I will add them to the list.Question Sheets:General or AllGrowing list of questions from mockinterview.io105 Data Science Interview Questions (General, ML)Machine Learning Questions (General)21 Data Science Interview Questions & Answers109 Data Science Interview Questions (General, many different topics, more links inside as well)ProbabilityAnswers to odd questions40 Questions and Answers on Probability for Data Science (Probability)Dartmouth Intro To Probability TextbookStatisticsNeed materialMathematicsLinear Algebra, Set Theory & Algebra, Numerical Methods & Calculus, Graph Theory, Combinatorics, Propositional & First Order Logic: Multiple Choice with AnswersMathematics of Machine Learning (a textbook still in development)Calculus of Multivariate Optimization (lecture sheet with examples)ProgrammingCracking the Coding Interview: 150 Programming Interview Questions with Solutions (textbook pdf)Computer Science Multiple Choice Questions & AnswersMore CS Multiple Choice Questions & AnswersMachine LearningSee general section.Andrew Ng’s Coursera Quizzes & AnswersReinforcement LearningReinforcement Learning: An Introduction, Sutton & Barto (online textbooks with problems/solutions)I will edit and update this posts as I find more resources, and if you can add any, please comment! 链接: 原文链接: via: