Hundreds of fully solved job interview questions from a wide range of key topics in AI.
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A Personal Note
Keep learning, or risk becoming irrelevant. 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 aimed, will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.
Corrections and Feedback
I would like to solicit corrections, criticisms, and suggestions from students and other readers. Although I have tried to eliminate errors over the multi-year process of writing and revising this text, a few undoubtedly remain. In particular, some typographical infelicities will no doubt make their way into the final version. I hope you will forgive them.
Contact Amir: LinkedIn | Google Scholar
Contact Shlomo: LinkedIn | Google Scholar
Where to Get the Book
This book is available for purchase through Amazon and other standard distribution channels. Please see the publisher’s web page to order the book or to obtain further details on its publication. A manuscript of the book can be found below—it has been made available for personal use only and must not be sold.
Download the PDF
The PDF is available here: arXiv Reference
About the Book
The second edition of Deep Learning Interviews (The Amazon Softcover is printed in BW) 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, with 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 this volume specifically valuable to students and job seekers: it provides them the ability to speak confidently and quickly on any relevant topic, answer technical questions clearly and correctly, and fully understand the purpose and meaning of interview questions and answers. Those are powerful, indispensable advantages to have when walking into the interview room.
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++.
Explanation through Analogy
Imagine embarking on a journey through a vast city—you could either wander aimlessly hoping to find your destination, or you could follow a detailed map. This book serves as that map for aspiring AI professionals. Each section strategically filters through the layers of complex AI topics like information theory and convolutional neural networks, much like how a map highlights the key routes to guide you efficiently to your destination. Just as a well-informed traveler understands the significance of major landmarks, this book equips you with vital knowledge to navigate the job interview landscape confidently.
Errata
Thank you to all the readers who pointed out these issues. The errata for the book’s versions include minor corrections, such as the removal of questions due to lack of clarity and amendments to specific texts for better clarity in solutions. Please refer to the book’s online platform for the most current corrections.
Troubleshooting
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