Deep Learning Papers Reading Roadmap: what four ordering rules produce
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
At a glance
- What is it?
- floodsung's Deep-Learning-Papers-Reading-Roadmap is one markdown file that orders deep learning papers by four fixed rules, running from a 2015 MIT Press book through ImageNet and speech work dated 2016. It is an index with an opinionated order, and its last commit is dated 2022-11-27.
- Who is it for?
- This list suits a reader who can already parse research papers and wants a curated starting order, and it fails the reader who needs prerequisites, a difficulty signal, or a current frontier. Before you commit weeks to it, check the entry dates yourself, expect no license terms, and treat the star marks as one person's private ranking rather than a difficulty scale.
- Can I use it commercially?
- Not without permission. GitHub finds no licence file in the repository, and without a licence all rights are reserved by default: you may read the code but not reuse it. Check the README, or ask the authors, before using it.
- Is it still maintained?
- Probably not. The repository last received commits 46 months ago, on November 27, 2022.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 25, 2026, and from our analysis. They are not legal advice.
Editorial analysis
Four ordering rules decide the whole sequence
The repository exists to answer one question, stated in the opening line: which paper should I start reading from. The answer is a fixed sequence built on four rules, from outline to detail, from old to state of the art, from generic to specific areas, and a focus on state of the art. Because those rules are global rather than per topic, the resulting order is the same for everybody who opens the file. The list starts with section 1, Deep Learning History and Basics, and subsection 1.0 holds exactly one item, the 2015 MIT Press book by Bengio, Goodfellow and Courville, linked to deeplearningbook.org, with a note that you can read the book while working through the papers that follow. Subsection 1.1 adds the LeCun, Bengio and Hinton survey from Nature 521.7553, 2015.
What the file cannot do is intake. It never asks about your background, your mathematics, or whether you have a GPU, and it contains no branch for a reader who already knows one domain. The order is a default, so if your prior experience is in computer vision the roadmap still starts you at a general history section, and if you have none it never tells you which prerequisite the first paper assumes. The author hands the decision back to you. The text after the first block says you can choose the following papers based on your interests and research direction. That handoff is where personalization would have to happen, and it happens in your head, not in the file.
ImageNet first, then speech, and the dates stop at 2016
Subsections 1.3 and 1.4 are the two long narrative runs, and they are where the state of the art rule actually bites. Subsection 1.3, labelled ImageNet Evolution and marked as the place deep learning broke out from, moves from AlexNet by Krizhevsky, Sutskever and Hinton in 2012, through VGGNet, GoogLeNet, and ResNet by He and others in 2015, with ResNet tagged as a CVPR best paper. Subsection 1.4, Speech Recognition Evolution, runs from the 2012 shared views paper by four research groups, through Graves and Jaitly's end to end work in 2014, Google's arXiv:1507.06947 in 2015, Baidu's Deep Speech 2, and Xiong and others on human parity in conversational speech recognition, arXiv:1610.05256 from 2016.
The newest dated entries shown in the roadmap are from 2016, and the last push to the repository is dated 2022-11-27. The consequence for a reader is that a rule written as focus on state of the art has a hard ceiling set by when the list was last touched, and that ceiling has not moved in close to four years. You get a curated slice of the convolutional and recurrent era, and nothing the author added afterwards. The ordering also encodes one historical thesis, that the field advanced through image classification and speech recognition, so any method that did not come up through those two tracks has no natural slot in the sequence.
download.py and a three line dependency list are the only code
The repository has three top level entries: README.md, download.py and requirements.txt. Everything a reader is meant to consume lives inside the markdown, and the dependency list is short enough to read in full. It pins lower bounds only, with no upper limits and no pinned versions:
mistune>=0.7.2
beautifulsoup4>=4.4.1
six>=1.10.0mistune and beautifulsoup4 are markdown and HTML parsing libraries, and six is the compatibility helper for code that still has to run on both Python 2 and Python 3.
The consequence is that there is no command to run anywhere. The README does not document how to install these packages, how to invoke download.py, what arguments it accepts, or where it puts any files it writes. Someone searching for a deep learning papers reading roadmap pdf lands on a list of links next to a script whose behavior is invisible. Clone the repository and you have the index, not the papers. Want local copies and you fetch them one URL at a time, with no checksums, no file naming convention, and no mirror recorded anywhere in the repository.
Star ratings carry the triage and nothing explains the scale
Most entries carry a parenthetical gloss and a run of star symbols, ranging from three stars up to five. The Deep Learning book, the Nature survey, AlexNet, and ResNet all get five. The 2006 deep belief network paper gets three, and the 2014 Graves and Jaitly end to end speech entry carries neither a gloss nor any stars at all.
The consequence is that the rating is the only triage device in the entire file, and its criteria are never stated anywhere. A five star mark on a 2006 milestone and a five star mark on a 2012 breakthrough sit in the same tier, so you cannot use the stars to budget time or to sequence a reading schedule. You also cannot distinguish an omission from a judgment, because an unrated entry might mean the author skipped it, ran out of time, or considered it ordinary, and the file offers no way to find out which. Plan to work in star order and you silently drop every unrated paper. Plan to work in the printed number order and the stars do nothing at all. Either way you are reading one maintainer's private ranking carried as a bare symbol with no legend and no scoring rule.
Every citation is a raw URL to a home page or an arXiv PDF
There is no DOI, no BibTeX key, and no venue identifier in the entries. The links resolve to a small set of hosts: deeplearningbook.org for the book, personal and group pages under cs.toronto.edu/~hinton/ for several Hinton papers, arxiv.org for the preprints, papers.nips.cc for the AlexNet conference paper, cv-foundation.org for GoogLeNet, jmlr.org for the ICML proceedings paper, and a pdf on semanticscholar.org for the binarized neural networks paper.
The consequence is that the list is a set of pointers to where a paper happened to be hosted, not a stable citation you can paste into a bibliography. Personal home pages and volunteer sites are the most fragile hosts in that set, and the file records no access date, so when a link fails you have no second address to try and no way to tell whether the paper moved, was renamed, or was withdrawn. Getting this list into a reference manager means visiting every entry by hand and typing the metadata yourself, and any tool built on top of the file has to scrape the prose rather than read structured fields.
No releases, no license, and a last push on 2022-11-27
The repository is not marked as archived, but its last push is dated 2022-11-27 and it has no GitHub releases. There is no tagged version, no changelog, and no way to pin the exact state you read. The README also states an intent to keep adding papers to the roadmap, and that intent has not been backed by a commit in close to four years. No license is recorded for the repository, so reuse terms for a curated list that other people clearly want are undefined.
The consequence is that forking is ambiguous. Any downstream project that wants a machine readable version of the ordering has to decide on its own what it may do with a list of links and one line glosses. For a reader the practical problem is drift: you cannot tell whether your fork is current, and you cannot diff your copy against the original without knowing which commit you started from. A resource that positions itself as a state of the art index needs a release cadence, or at least a date stamp on each section, to be trustworthy, and the file has neither.
No prerequisites, no difficulty markers, no orientation for a newcomer
The opening question is which paper to start from, and the answer quietly assumes you can already read an arXiv paper in English and follow the notation in a Nature survey. The file contains no definition of a deep neural network, no prerequisites in calculus, linear algebra, or probability, and no estimate of how long any entry takes. The text after the first block says that reading those papers will give you a basic understanding of the basic architectures of a deep learning model, including CNN, RNN, and LSTM, which means the list expects the papers themselves to carry the entire teaching load from page one.
The consequence is that this is a reading list for a reader who can already parse research papers, not an entry point for a reader deciding whether the field is for them. If you want to know what a DNN is or how hard the subject is, this repository settles neither question, and your branch point after section 1 is chosen from the section titles alone. What the file gives you is a starting point and an opinion about order. What it cannot give you is a reason for the order, a prerequisite check, or any signal that the frontier moved after its last dated entry.
Editorial conclusion
This list suits a reader who can already parse research papers and wants a curated starting order, and it fails the reader who needs prerequisites, a difficulty signal, or a current frontier. Before you commit weeks to it, check the entry dates yourself, expect no license terms, and treat the star marks as one person's private ranking rather than a difficulty scale. Anyone automating it will have to scrape the prose, because the only machine-readable file in the repository is a three line dependency list.
Frequently asked questions
What is a roadmap for learning deep learning in this repository?
It is a single markdown file ordering deep learning papers by four rules: from outline to detail, from old to state of the art, from generic to specific areas, and a focus on state of the art. The newest dated entries shown run from a 2015 textbook through ImageNet and speech papers up to arXiv:1610.05256 from 2016.
How does the Deep Learning Papers Reading Roadmap handle explaining a DNN to a beginner?
It does not define terms. The file assumes you can read a research paper and states that reading the first block gives you a basic understanding of architectures including CNN, RNN, and LSTM, so the teaching is expected to come from the papers rather than from the list.
Is deep learning very difficult according to the Deep Learning Papers Reading Roadmap?
The file carries no difficulty markers. Entries are annotated with a short gloss and a run of star symbols, but no scale is defined, so the stars cannot be used to judge how hard a paper is or how long it will take you.
Does the Deep Learning Papers Reading Roadmap cover ChatGPT?
The entries shown end at Xiong and others on human parity in conversational speech recognition, arXiv:1610.05256 from 2016, and the last push to the repository is dated 2022-11-27. Nothing in the list addresses ChatGPT, so the file cannot be used to answer that question.