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Deep Learning Interviews book: Hundreds of fully solved job interview questions

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In this first volume, I purposely present a coherent, cumulative, and content-specific core curriculum of the data science field, including topics
such as information theory, Bayesian statistics, algorithmic differentiation, logistic regression, perceptrons, and convolutional neural networks.
I hope you will find this book stimulating.

It is my belief that you the postgraduate students and job-seekers for whom the book is primarily meant will benefit from
reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.

Follow up for more updates on our work:


The PDF is available here:

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https://arxiv.org/abs/2201.00650

Citation

@misc{kashani2021deep,
      title={Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI}, 
      author={Shlomo Kashani and Amir Ivry},
      year={2021},
      eprint={2201.00650},
      note = {ISBN 13: 978-1-9162435-4-5 }, 
      url = {https://www.interviews.ai}, 
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

SELLING OR COMMERCIAL USE IS STRICTLY PROHIBITED.
The user rights of this e-resource are specified in a licence agreement below.
You may only use this e-resource for the purposes private study.
Any selling/reselling of its content is strictly prohibited.

This book (www.interviews.ai) was written for you: an aspiring data scientist with a quantitative background, facing down the gauntlet of the interview process in an increasingly competitive field. For most of you, the interview process is the most significant hurdle between you and a dream job.
Even though you have the ability, the background, and the motivation to excel in your target position, you might need some guidance on how to get your foot in the door.

About

The second edition of Deep Learning Interviews (The Amazon Softcover is printed in B&W) 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.

  • The book spans almost 400 pages
  • Hundreds of fully-solved problems
  • Problems from numerous areas of deep learning
  • Clear diagrams and illustrations
  • A comprehensive index
  • Step-by-step solutions to problems
  • Not just the answers given, but the work shown
  • Not just the work shown, but reasoning given where appropriate

This book was written for you: an aspiring data scientist with a quantitative background, facing down the gauntlet of the interview process in an increasingly competitive field. For most of you, the interview process is the most significant hurdle between you and a dream job. Even though you have the ability, the background, and the motivation to excel in your target position, you might need some guidance on how to get your foot in the door.
Your curiosity will pull you through the book’s problem sets, formulas, and instructions, and as you progress, you’ll deepen your understanding of deep learning. There are intricate connections between calculus, logistic regression, entropy, and deep learning theory; work through the book, and those connections will feel intuitive.

CORE SUBJECT AREAS (VOLUME-I):

VOLUME-I of the book focuses on statistical perspectives and blends background fundamentals with core ideas and practical knowledge. There are dedicated chapters on:

  • Information Theory
  • Calculus & Algorithmic Differentiation
  • Bayesian Deep Learning & Probabilistic Programming
  • Logistic Regression
  • Ensemble Learning
  • Feature Extraction
  • Deep Learning: expanded chapter (100+ pages)

These chapters appear alongside numerous in-depth treatments of topics in Deep Learning with code examples in PyTorch, Python and C++.

Disclaimers

  • “PyTorch” is a trademark of Facebook.

Licensing

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2 Comments

  1. I actually bought this as a physical book on Amazon. Naturally it came as a print-on-demand book. Unfortunately it has many problems in this format. E.g. the lack of margins makes it hard to read the end of sentences towards the gutter. Also some text is pushed into each other. Not sure what source file format you have to provide to Amazon, but it's certainly not the pdf provided in the repo.

    Edit:

    It seems the overlapping text also occurs on some pdf readers:
    https://github.com/BoltzmannEntropy/interviews.ai/issues/2

  2. This book has fun problems! Example:

    During the cold war, the U.S.A developed a speech to text (STT) algorithm that could theoretically detect the hidden dialects of Russian sleeper agents. These agents (Fig. 3.7), were trained to speak English in Russia and subsequently sent to the US to gather intelligence. The FBI was able to apprehend ten such hidden Russian spies and accused them of being "sleeper" agents.

    The Algorithm relied on the acoustic properties of Russian pronunciation of the word (v-o-k-s-a-l) which was borrowed from English V-a-u-x-h-a-l-l. It was alleged that it is impossible for Russians to completely hide their accent and hence when a Russian would say V-a-u-x-h-a-l-l, the algorithm would yield the text "v-o-k-s-a-l". To test the algorithm at a diplomatic gathering where 20% of participants are Sleeper agents and the rest Americans, a data scientist randomly chooses a person and asks him to say V-a-u-x-h-a-l-l. A single letter is then chosen randomly from the word that was generated by the algorithm, which is observed to be an "l". What is the probability that the person is indeed a Russian sleeper agent?

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