README.md
/README.md
A single top-level heading, “100”. No description, audience statement, or philosophy is written here — the repository's real content lives in the two files beside it.
1 line7 bytes
github.com/mohdshamii/100
Two hundred-item reading lists for learning AI and ML, beginner to advanced.
The repository holds two parallel, hand-curated collections — 100 books and 100 research papers — each split into ten thematic categories and each shipped with its own suggested order of study.
2 content files
200 total entries
10 + 10 categories
2 suggested study paths
README.md7 bytes
Books.md135 lines · 12.7 KB
ResearchPaper.md135 lines · 10.7 KB
Default branchmain
A note on this page. The repository's
README.md currently contains only the heading “100” — it doesn't state a mission, audience, or philosophy. Everything below is derived directly from the actual content of Books.md and ResearchPaper.md, not from marketing copy.
0
Books, in Books.md
0
Papers, in ResearchPaper.md
0
Categories per file
0
Stages in each suggested path
Repository ecosystem
Three files, two reading tracks
Everything in the repository lives in one of three markdown files. README.md is a placeholder; the two content files are what carry the actual library.
Books.md
/Books.md
“100 AI/ML Books — Beginner to Advanced.” Ten numbered sections running from math foundations to alignment and safety, plus a month-by-month suggested path.
MathClassic MLDeep LearningNLPCVRLLLMsMLOpsTheory
ResearchPaper.md
/ResearchPaper.md
“100 AI/ML Research Papers — Beginner to Advanced.” Ten numbered sections from 1958's Perceptron paper to 2023 alignment and evaluation work, plus a week-by-week reading order.
FoundationsCNNsRNN/LSTMTransformersLLMsGenerativeRLSelf-SupervisedMultimodalSafety
Books.md
runs parallel to
ResearchPaper.md
— both move beginner → advanced across ten matched-in-spirit sections, each with its own independent suggested schedule.
Interactive resource explorer
Search and browse every category
Filter by track, search by category or by any of the representative titles pulled from the file, and expand a category to see examples and jump straight to that section on GitHub.
No categories match that search.
Suggested study paths
Two independent schedules, one per file
Books.md and ResearchPaper.md each end with their own “suggested path” — one paced by month, the other by week. Neither file instructs you to interleave them; they're shown side by side here because they cover comparable ground in a comparable order.
Books.md — “Suggested path”
by monthResearchPaper.md — “Suggested reading order”
by weekHow to use this repository
What the files actually tell you to do
The repository has no separate onboarding guide, so this is assembled from the notes and suggested-path sections written directly into Books.md and ResearchPaper.md.
Books.md
Start with math and classic ML
The suggested path opens with Section 1 (Math & Stats Foundations) alongside Section 2 (Classic Machine Learning) across months 1–2, then Sections 3–4 (deep learning foundations and practical engineering) in months 3–4.
Books.md
Pick a specialty, then go current
Months 5–6 are for choosing NLP (§5), computer vision (§6), or reinforcement learning (§7). Month 7 moves to generative AI and LLMs (§8), which the file flags as most relevant to current industry roles, before MLOps (§9) and theory (§10) from month 8 onward.
ResearchPaper.md
Read chronologically through the fundamentals
Weeks 1–3 cover Sections 1–3: classic foundations, CNN architectures, and the RNN/LSTM era — the historical build-up to modern deep learning.
ResearchPaper.md
Prioritize transformers and LLMs
Weeks 4–6 cover Sections 4–5 (attention, transformers, and large language models), which the file calls out as most relevant for AI/ML engineering roles today, before generative models, RL, self-supervision, and finally multimodal/safety work in weeks 7–12.
Both files
Links favor free, permanent sources
Books.md links to an author's official free version or open textbook where one exists; ResearchPaper.md links mostly to direct arXiv PDFs, noting that a handful of pre-2012 classics link to their original hosting page instead.
Both files
If a link breaks
Each file gives the same fallback: search the title and author (Books.md) or the title on Google Scholar (ResearchPaper.md) rather than assuming the resource is gone.
No contribution guide yet
The repository doesn't include a CONTRIBUTING.md, so there's no documented process to summarize here. The most reliable way to suggest a resource or flag a broken link is directly on GitHub.