stanford-tensorflow-tutorials
Code examples from Stanford's CS 20 course on using TensorFlow for deep learning research.
- Stars
- 10.4k
- Forks
- 4.2k
- Licence
- MIT
- Language
- Python
- Last update
- 5.9 years ago
- Type
- Learning resource
This repository is archived and no longer maintained.
What it is
This is a collection of Python code examples that accompany Stanford's CS 20 course, TensorFlow for Deep Learning Research. It is meant to be read alongside the course lectures and notes, which are linked from the course website. The repository is archived, and the README says it was written for Python 3.6 and TensorFlow 1.4.1.
- Read through the example code to see how TensorFlow models were structured in the course
- Run the course examples as a hands-on exercise, using the older versions the README specifies
- Use the 2017 folder to compare against the previous year's course materials
Great for
- Students following the original CS 20 course materials
- Learners who want working TensorFlow examples to study and modify
- People interested in the NLP and chatbot examples included in the topics
Not ideal for
- Anyone wanting current TensorFlow code: the README targets TensorFlow 1.4.1 and the repository is archived, so newer TensorFlow versions may not work without changes
- People looking for a maintained library or production tool, since the project is archived and no longer updated
How to get it
- 1
Clone and build from source
You'll need: Python 3.6 · TensorFlow 1.4.1
Clone the repository
git clone https://github.com/chiphuyen/stanford-tensorflow-tutorials.git - 2
Run it
- Clone the repository and open the setup folder to find the dependency list
- Install Python 3.6 and TensorFlow 1.4.1 as described in the setup instructions
- Open the course website at http://cs20.stanford.edu to find the lecture that matches each example
- Run the example scripts from the lecture topic you are studying
- 3
Get the most out of it
- Read the course syllabus and lecture notes on the course website alongside the code
- Compare the 2017 folder with the current examples to see how the course changed
- Study the chatbot and NLP examples, which are listed in the repository topics
Downloads
Strengths
- Comes from a well-known university course with published syllabus and lecture notes
- Released under the MIT license, so the code can be reused freely
- Has a large following, with about 10,000 stars on GitHub
Trade-offs
- Archived, with the last push in December 2020, so it is not maintained
- Built for TensorFlow 1.4.1 and Python 3.6, which are long out of date
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| stanford-tensorflow-tutorials (this) | 10.4k | Learning resource | intermediate | 5.9 years ago | Students following the original CS 20 course materials |
| nlp-tutorial | 14.9k | Learning resource | intermediate | 2.7 years ago | Students who want to see how classic NLP models work in code |
| AI-For-Beginners | 70.5k | Learning resource | beginner | 13 days ago | Beginners who want a structured, self-paced introduction to AI |
| Made-With-ML | 49.9k | Learning resource | intermediate | 8 months ago | Software engineers who want to learn how ML models get deployed and maintained |
| mit-deep-learning | 10.5k | Learning resource | beginner | 2.8 years ago | Students following MIT deep learning courses |
| spaCy | 34k | Library | intermediate | 10 days ago | Python developers building text-processing features into an application |
Stats synced from GitHub. The assessment above was drafted with AI from the project's README on 2026-10-29 and may miss recent changes — check the README before relying on a command. MIT licence: free to use, modify and distribute, including commercially, as long as you keep the copyright notice.