# BoltzmannEntropy/interviews.ai

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.

Repository: https://github.com/BoltzmannEntropy/interviews.ai
Canonical: https://ross.abutalabs.com/products/interviewsai
Homepage: https://interviews.ai
License Family: other
Topics: data-science, machine-learning, deep-learning, interview-preparation, jobs, artificial-intelligence, pytorch-tutorial, graduate-school, pytorch, python, jax, autograd, information-theory, bayesian-statistics, convolutional-neural-networks, ensemble-learning, feature-extraction, logistic-regression, loss-functions
Last push: 2025-08-22T07:13:40+00:00

## Health v2 (maintenance only)
Score: 49/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 38, release rhythm 35, longevity 100
- inputs: {"age_days": 1798, "days_push": 376, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4890, forks 325 (observed 2026-08-28T04:09:01.987039+00:00)

## What it is
An open book, 'Deep Learning Interviews', containing hundreds of fully solved job interview questions across key AI and data science topics, available as a free PDF and in print. The repository hosts the book's manuscript, errata, and supporting material for graduate students and job seekers.

## Use cases
- prepare for a machine learning engineer job interview
- study deep learning interview questions with solutions
- review information theory and Bayesian statistics for AI exams
- practice PyTorch and autograd questions before an interview
- brush up on logistic regression, CNNs, and ensemble learning
- find a free deep learning textbook PDF for graduate study

## When to choose
- you are a student or job seeker preparing for AI/ML interviews or graduate exams
- you want fully worked solutions rather than just question lists
- you need a free, citable PDF covering math foundations through CNNs

## When to avoid
- you need hands-on code libraries or production tooling rather than a book
- you want a structured course with exercises graded automatically
- you need content under a permissive license for commercial use (selling is prohibited)

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, data-science, artificial-intelligence, education, tutorials
- platform: cross-platform
- tags: interview-preparation, book, pytorch, jax, bayesian-statistics, information-theory, cnn, ensemble-learning, free-pdf, arxiv, education

## Member repositories
- BoltzmannEntropy/interviews.ai (main) score 49

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:01.987039+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:18:11.503659+00:00, confidence not recorded.
  - readme: https://github.com/BoltzmannEntropy/interviews.ai (fetched 2026-08-28T04:09:01.987039+00:00, sha ca21b06c7656)
  - homepage: https://interviews.ai (fetched 2026-08-29T09:00:50.185219+00:00, sha 31874e230f08)
- Data as of 2026-08-30T08:39:29.467469+00:00.