Guide · Open source
How to use TensorFlow-Course: clone and run the notebooks
Updated · written with AI from the project's own documentation and checked against our catalogue data
TensorFlow-Course is an open-source collection of TensorFlow tutorials written as Jupyter Notebook files, published by instillai on GitHub under the MIT licence. This guide is for beginners and students who want to learn TensorFlow by running example notebooks. By the end, you will know what you need installed, how to get the repository onto your machine, and what to check before relying on the material.
TensorFlow-Courseinstillai/TensorFlow-Course · ★ 16.3k · MIT Install options & downloads Quick start
- 1Install the prerequisites. Install Python and Jupyter Notebook using the official instructions for your system.
- 2Clone the repository. Run git clone https://github.com/instillai/TensorFlow-Course in the folder where you want the project.
- 3Start Jupyter. Launch Jupyter Notebook from inside the cloned TensorFlow-Course folder.
- 4Open a notebook. Pick a notebook file from the folder and open it in your browser.
- 5Run the cells in order. Execute the cells from top to bottom so each example has the code it depends on.
What you need before you start
The repository lists two prerequisites: Python and Jupyter Notebook. The source material does not give installation commands for either, so use the official instructions for your operating system to set them up.
- Python
- Jupyter Notebook
Get the repository onto your computer
The project is installed by cloning it with Git. Run the command below in a terminal, in the folder where you want the project to live. This creates a local copy of the repository.
git clone https://github.com/instillai/TensorFlow-CourseOpen and run the tutorial notebooks
The tutorials are stored as Jupyter Notebook files. After cloning, start Jupyter in the project folder and open the notebooks from there. Run the cells in order, since later examples can depend on earlier ones.
The README in the material provided was empty, so the source does not list the individual lessons or topics. Check the file names in the cloned folder to see what each notebook covers.
Check the material against current TensorFlow documentation
The source does not state which TensorFlow version the notebooks use, and the repository has not been updated recently. Treat the code as a starting point. If a cell fails or an API call looks unfamiliar, consult the current TensorFlow documentation before assuming the notebook is wrong.
The source names the TensorFlow official tutorials as a comparable resource. Using them alongside these notebooks is a practical way to confirm current API usage.
- Note any error messages and compare the function names with the official docs.
- Keep your own copy of changes you make, since the repository is a learning resource rather than a maintained library.
What people use it for
- Learning the basics of TensorFlow by running example notebooks line by line.
- Using the notebooks as a first hands-on exercise in a beginner deep learning course.
- Adapting a notebook's code as a starting point for a small personal experiment.
Frequently asked questions
Is TensorFlow-Course free to use?
The repository is published under the MIT licence, which is a permissive open-source licence. Check the LICENSE file in the repository for the full terms before reusing the code.
What do I need to run TensorFlow-Course?
The repository lists Python and Jupyter Notebook as prerequisites. You clone the repository with Git and then open the notebook files in Jupyter.
How do I install TensorFlow-Course?
Run git clone https://github.com/instillai/TensorFlow-Course in your terminal. There is no separate installer, so the repository itself is the installation.
Is TensorFlow-Course up to date with the latest TensorFlow?
The source does not say which TensorFlow version the notebooks use, and the repository has not been updated recently. Verify function names and outputs against the current TensorFlow documentation.
What are alternatives to TensorFlow-Course?
The source lists the TensorFlow official tutorials, fast.ai course materials and Dive into Deep Learning as comparable projects. Each covers deep learning learning material from a different angle.
Can I use TensorFlow-Course in production?
No. The project is a collection of tutorials rather than a production library or framework, so use TensorFlow itself for production work.
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Our verdict
TensorFlow-Course suits beginners and students who want to learn TensorFlow by running notebooks in Jupyter, and its MIT licence makes it easy to reuse. It is less suitable if you need current API guidance or a production tool, because the source does not state the TensorFlow version used and the repository has not been updated recently. For those needs, the official TensorFlow tutorials are the safer choice.
Install options & downloads