TensorFlow-Course
Beginner-friendly Jupyter notebook tutorials for learning TensorFlow.
- Stars
- 16.3k
- Forks
- 3.1k
- Licence
- MIT
- Language
- Jupyter Notebook
- Last update
- 3.9 years ago
- Type
- Learning resource
No commits in over a year — check for forks or alternatives before adopting.
What it is
This repository is a collection of tutorials for TensorFlow, a machine learning library, written as Jupyter Notebook files. Based on its description, it aims to be simple and ready to use. The README was empty in the material provided, so details about the specific lessons and topics could not be verified.
- Open the notebooks in Jupyter to follow TensorFlow examples step by step
- Use the notebooks as a starting point for your own deep learning experiments
Great for
- People who want to learn TensorFlow by running example notebooks
- Students starting out with deep learning in Python
- Developers who prefer learning from hands-on notebooks
Not ideal for
- Readers who need up-to-date TensorFlow API guidance, since the last push was 2022-11-28 and the TensorFlow version used is not stated
- Anyone looking for a production library or framework, since this is a tutorial collection rather than a tool
How to get it
- 1
Clone and build from source
You'll need: Python · Jupyter Notebook
Clone the repository
git clone https://github.com/instillai/TensorFlow-Course - 2
Run it
- Clone the repository to your machine
- Install Python and Jupyter Notebook
- Open the repository folder in Jupyter
- Run the notebooks in order
- 3
Get the most out of it
- Check the notebook dependencies against your installed TensorFlow version before running
- Compare examples with the official TensorFlow documentation to confirm current APIs
Downloads
Strengths
- Released under the MIT license, which permits reuse
- Written as Jupyter notebooks, so code and explanations sit together
- Has a large following on GitHub, with about 16,000 stars
Trade-offs
- The README was not available in the provided material, so setup steps are unverified
- The last push was in November 2022, so examples may not match current TensorFlow versions
How it compares
| Project | Stars | Type | Level | Updated | Best for |
|---|---|---|---|---|---|
| TensorFlow-Course (this) | 16.3k | Learning resource | beginner | 3.9 years ago | People who want to learn TensorFlow by running example notebooks |
| annotated_deep_learning_paper_implementations | 67.6k | Learning resource | intermediate | 4 days ago | Students learning how transformers, GANs, or optimizers work internally |
| mit-deep-learning | 10.5k | Learning resource | beginner | 2.8 years ago | Students following MIT deep learning courses |
| stanford-tensorflow-tutorials | 10.4k | Learning resource | intermediate | 5.9 years ago | Students following the original CS 20 course materials |
| the-incredible-pytorch | 12.7k | Curated list | beginner | 6 days ago | Beginners looking for PyTorch tutorials and example projects to work through |
| netron | 33.6k | App | beginner | 5 days ago | Engineers who need to check a model's layers and tensor connections before deploying it |
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.