Deep Learning Interviews (2021)
The paper "Deep Learning Interviews" by Shlomo Kashani and Amir Ivry offers a resource for AI job seekers, featuring solved interview questions to enhance understanding and confidence in technical discussions.
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The paper titled "Deep Learning Interviews" by Shlomo Kashani and Amir Ivry presents a comprehensive resource for job seekers and students in the field of artificial intelligence (AI) and machine learning. This second edition contains hundreds of fully solved interview questions covering a wide range of key topics in AI, aimed at helping individuals prepare for interviews or exams. The content is designed to enhance understanding and confidence in discussing relevant topics, enabling candidates to answer technical questions clearly and accurately. The book serves as a valuable reference for MSc and PhD students, providing a structured overview of essential concepts in machine learning and AI. It emphasizes the importance of practical mathematical and computational skills in computer science education, aligning with the growing trend of integrating AI into university curricula. The volume is positioned as a critical tool for those entering the job market, equipping them with the necessary knowledge and skills to succeed in interviews.
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3 commentsI find her book to be more practical and humble, though perhaps less feature-complete than the book from this post.
IMO Kashani and Ivry’s writing feels a little uppity and needlessly offputting — for example, logistic regression is lumped under Kindergarten in the table of contents. Sure it’s fundamental, but implying it’s not useful anymore in our age of deep learning (listed under “Bachelor’s,” of course) is a little myopic/insulting, no? Students who feel pandered to by know-it-alls probably learn worse than students whose skills are being collaboratively built up by enthusiastic mentors.
This feeling was particularly loud from the foreward, ToC, and intro; maybe it gets better after…?
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