Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI

• URL: http://arxiv.org/abs/2201.00650v1
• Date: Thu, 30 Dec 2021 13:28:27 GMT
• Title: Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI
• Authors: Shlomo Kashani, Amir Ivry
• Abstract summary: Deep Learning Interviews is designed to both rehearse interview or exam specific topics and provide a well-organized overview of the field. The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML.
• Score: 2.0305676256390934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The second edition of Deep Learning Interviews is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific topics and provide machine learning M.Sc./Ph.D. students, and those awaiting an interview a well-organized overview of the field. The problems it poses are tough enough to cut your teeth on and to dramatically improve your skills-but they're framed within thought-provoking questions and engaging stories. That is what makes the volume so specifically valuable to students and job seekers: it provides them with the ability to speak confidently and quickly on any relevant topic, to answer technical questions clearly and correctly, and to fully understand the purpose and meaning of interview questions and answers. Those are powerful, indispensable advantages to have when walking into the interview room. The book's contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs.

Related papers

• GPT-Powered Elicitation Interview Script Generator for Requirements Engineering Training [0.0]
  We develop a specialized GPT agent for auto-generating interview scripts. The GPT agent is equipped with a dedicated knowledge base tailored to the guidelines and best practices of requirements elicitation interview procedures. We employ a prompt chaining approach to mitigate the output length constraint of GPT to be able to generate thorough and detailed interview scripts.
  arXiv  Detail & Related papers  (2024-06-17T11:53:55Z)
• Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training [65.10741459705739]
  We propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo. We first design two-level question augmentations, including content-level and structure-level, which generate literally diverse question pairs with similar purposes. Then, to fully exploit hierarchical information of knowledge concepts, we propose a knowledge hierarchy-aware rank strategy.
  arXiv  Detail & Related papers  (2023-01-18T14:23:29Z)
• EZInterviewer: To Improve Job Interview Performance with Mock Interview Generator [60.2099886983184]
  EZInterviewer aims to learn from the online interview data and provides mock interview services to the job seekers. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs.
  arXiv  Detail & Related papers  (2023-01-03T07:00:30Z)
• Asking for Knowledge: Training RL Agents to Query External Knowledge Using Language [121.56329458876655]
  We introduce two new environments: the grid-world-based Q-BabyAI and the text-based Q-TextWorld. We propose the "Asking for Knowledge" (AFK) agent, which learns to generate language commands to query for meaningful knowledge.
  arXiv  Detail & Related papers  (2022-05-12T14:20:31Z)
• Machine Learning for Computational Science and Engineering -- a brief introduction and some critical questions [0.0]
  This is a general-purpose article written for a general audience and researchers new to the fields of Machine Learning and/or Computational Science and Engineering. Some basic equations and code are also provided to help the reader understand the basics.
  arXiv  Detail & Related papers  (2021-12-22T17:25:32Z)
• Achieving Human Parity on Visual Question Answering [67.22500027651509]
  The Visual Question Answering (VQA) task utilizes both visual image and language analysis to answer a textual question with respect to an image. This paper describes our recent research of AliceMind-MMU that obtains similar or even slightly better results than human beings does on VQA. This is achieved by systematically improving the VQA pipeline including: (1) pre-training with comprehensive visual and textual feature representation; (2) effective cross-modal interaction with learning to attend; and (3) A novel knowledge mining framework with specialized expert modules for the complex VQA task.
  arXiv  Detail & Related papers  (2021-11-17T04:25:11Z)
• A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers [66.11048565324468]
  We present a dataset of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers.
  arXiv  Detail & Related papers  (2021-05-07T00:12:34Z)
• Inquisitive Question Generation for High Level Text Comprehension [60.21497846332531]
  We introduce INQUISITIVE, a dataset of 19K questions that are elicited while a person is reading through a document. We show that readers engage in a series of pragmatic strategies to seek information. We evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions.
  arXiv  Detail & Related papers  (2020-10-04T19:03:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.