# amanchadha/coursera-deep-learning-specialization: a working copy of the Andrew Ng assignments > The repository collects the notebooks, quizzes and a setup script from all five courses of the Coursera Deep Learning specialization. It is a study and reference copy, not a maintained library, and the README does not state a licence. **amanchadha/coursera-deep-learning-specialization** — Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models - Repository: https://github.com/amanchadha/coursera-deep-learning-specialization - Stars: 4,381 · Forks: 2,676 - Language: Jupyter Notebook - License: not declared - Published: 2026-09-10 · Updated: 2026-09-10 · Language: en - Canonical page: https://hysenlabs.com/projects/amanchadha-coursera-deep-learning-specialization ## What the repository actually contains, course by course This is one person's completed coursework, published as a GitHub repository. The top level mirrors the specialization's structure: five directories, one per course, from C1 - Neural Networks and Deep Learning through C5 - Sequence Models, plus a loose notebook named Neural machine translation with attention_latest.ipynb at the root. Inside the course folders, the README lists assignments week by week with links to nbviewer, so a reader can open a rendered notebook in a browser without cloning anything. The audience is narrow and obvious. Someone taking the specialization who wants to see a finished version of an assignment. Someone who finished it and wants the notebooks in one place for later reference. Someone preparing for interviews who, per the README, is pointed to www.aman.ai for what it calls detailed interview-ready notes. It is not a library you import. Nothing in the repository is packaged, versioned or published to a registry. Course 3 is the exception to the pattern. The README states plainly that Structuring Machine Learning Projects has no programming assignments, only case study quizzes. Anyone browsing the folder tree expecting notebooks there will find quizzes instead, and the README says so rather than leaving the gap unexplained. ## How the setup script and notebook layout work The mechanism is file delivery, not code execution. setup.sh does two things according to the README: it downloads a pre-trained VGG-19 dataset and it extracts zipped pre-trained models and datasets needed across the assignments. That is the whole runtime story. Once the archives are unpacked, each assignment is a self-contained Jupyter notebook that expects its data to sit in a known relative path. The consequence is that the repository is only useful in a full notebook environment. There is no CLI, no server, no API surface, no configuration file to tune. If you open a notebook without having run setup.sh, the cells that load pre-trained models will fail on a missing path. That is the most common way this repository breaks for a new user, and it is a direct result of shipping data as an extracted archive rather than as a dependency. The README also notes that the specialization was updated in April 2021, with the largest change being the move from TensorFlow 1 to TensorFlow 2, and that the repository was updated to match. That matters because the notebook filenames carry version suffixes such as v3a, v6a, v8a and v2a. Those suffixes are the course's own versioning, not the repository's, and they are the only signal you get about which revision of an assignment a file corresponds to. ## Cloning it and running the first notebook There is no install step in the usual sense. The README gives one instruction: run setup.sh. Clone the repository, change into it, and run the script. The script needs network access for the VGG-19 download and enough disk space for the extracted archives. ```bash git clone https://github.com/amanchadha/coursera-deep-learning-specialization.git cd coursera-deep-learning-specialization bash setup.sh ``` After the script finishes, the pre-trained models and datasets should be present alongside the course folders. Start Jupyter from the repository root so that the relative paths inside the notebooks resolve, then open one of the early assignments. The first course's Week 2 Python Basics with Numpy notebook is the least demanding entry point because it does not depend on the pre-trained models. In the browser, navigate to C1 - Neural Networks and Deep Learning, then Week 2, then the Python Basics with Numpy folder, and open the .ipynb file. You should see the assignment cells with the exercise functions already filled in. Run the cells top to bottom; the expected output is printed beneath each one. If a later notebook fails on a file path, the cause is almost always that setup.sh was not run from the repository root. ## The licence gap is the real problem, not the code The repository has no licence file. The README's Credits section says the code base, quiz questions and diagrams are taken from the Coursera Deep Learning specialization unless specified otherwise. Those two facts together mean a reader has no grant of rights from this repository, and the upstream material carries its own terms from Coursera and deeplearning.ai. A personal clone for study is one thing. Redistributing the notebooks, reusing the quiz questions in your own course, or shipping the diagrams inside a product is a different question, and this repository does not answer it. This is not a technicality to wave away. A repository that mirrors course content inherits the ambiguity of that content, and the absence of a LICENSE file is the clearest signal available that the author did not resolve it. If you need material you can legally build on, the safe move is to write your own notebooks from the course itself rather than copy these. The same gap applies to the pre-trained models that setup.sh fetches. The README describes what the script downloads but does not state the terms attached to those weights. Treat them as course material, not as a free asset. ## Where it stops being the right tool This repository will not help you build anything. There is no package to install, no importable module, no test suite, no CI configuration visible in the top-level entries beyond a .github directory. If your goal is a working image classifier, a segmentation model or a translation system, the notebooks show you how the pieces fit together but they are teaching artifacts, not production code. The MobileNet transfer learning notebook and the U-Net segmentation notebook are demonstrations with course-sized datasets, not starting points for a deployment. A second limitation is freshness. The last push to the default branch was on 2026-06-10. That is recent enough that the repository is not abandoned, but the content inside it descends from a specialization updated in April 2021. Frameworks move faster than courses. A notebook written against TensorFlow 2 as it existed in 2021 may run without modification today, or it may not, and nothing in the repository tells you which. There are no releases, so there is no changelog to check against. A third issue is that the notebooks are complete. If you are currently enrolled and working through an assignment, the answers are right there, which defeats the exercise. That is a study-habit problem rather than a defect, but it is worth naming because the repository's main draw for an enrolled student is also its main hazard. ## How it differs from the official course material The obvious alternative is the specialization itself on Coursera, and the difference is not just price. The course gives you graded feedback, a verified certificate, and the current revision of each assignment. This repository gives you a static copy of one person's completed work with no grading loop. If you want the certificate or you want to be sure you are solving the assignment as it is currently posed, the course is the only source. A second alternative is the official deeplearning.ai repositories and the TensorFlow tutorials that the course itself draws on. Those are maintained by their authors and carry explicit licences. The trade-off is coverage: no single official repository collects all five courses' assignments in one tree the way this one does. You gain clear terms and lose the convenience of a single clone. A third option, for someone who only wants the concepts, is the notes site the README points to at www.aman.ai. That is prose rather than runnable notebooks, which suits interview preparation better than it suits hands-on practice. The three options solve different problems: credential, runnable code, and condensed reading. ## Maintenance and what an upgrade would cost you There are no releases in the repository, so there is no version to pin and no upgrade path to follow. The only maintenance signal is the push history, and the last push was on 2026-06-10. The repository is not archived, so it has not been formally retired, but nothing in the README commits the author to tracking future changes to the specialization or to the frameworks it uses. In practice this means you fork or you do not. If a notebook breaks because a TensorFlow API changed, you fix it in your own copy, and your copy diverges from the original with no mechanism to merge back. For a study repository that is an acceptable cost. For anything you intend to keep running, it is the reason to look elsewhere. The licence situation compounds the maintenance question. With no LICENSE file and course-derived content, you cannot assume you have the right to redistribute your fixed version, which makes the fork-and-share path legally unclear even if the technical path is trivial. ## Conclusion Use this repository if you are working through the deeplearning.ai specialization and want a second copy of the notebooks to compare against, or if you want to read someone else's completed assignments without enrolling. Do not use it as a dependency, a dataset source, or a base for a product: there is no licence file, no release, and no maintenance commitment beyond the last push on 2026-06-10. Before you clone, check the README's Credits section, which says the code base, quiz questions and diagrams come from the Coursera specialization, and decide for yourself whether redistributing that material is something you want to do. ## FAQ ### Why is Coursera falling? The repository does not discuss Coursera's business. It only links to the Deep Learning specialization page and notes that the course material, quiz questions and diagrams come from that specialization. ### Is the Coursera Machine Learning Specialization worth it? The repository does not evaluate the specialization. It names Andrew Ng as the instructor and points to www.aman.ai for interview-ready notes, but makes no claim about whether the course is worth the time. ### Do we get a specialization certificate in Coursera? The repository does not address certificates. It contains programming assignments and quizzes, and its Credits section says the code base, quiz questions and diagrams are taken from the Coursera Deep Learning specialization unless specified otherwise. ### Are Coursera certificates taken seriously? The repository takes no position on this. It is one person's coursework for the Deep Learning specialization, with no statement about how certificates are regarded. ## Sources - [amanchadha/coursera-deep-learning-specialization on GitHub](https://github.com/amanchadha/coursera-deep-learning-specialization) - [Issues](https://github.com/amanchadha/coursera-deep-learning-specialization/issues) - [README](https://github.com/amanchadha/coursera-deep-learning-specialization/blob/master/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/amanchadha-coursera-deep-learning-specialization