Showcase Your TensorFlow Skills with a GitHub README Badge

Use this markdown in your README's tech stack section:

![TensorFlow](https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge&logo=tensorflow&logoColor=white)

This renders the official orange badge (#FF6F00) with the TensorFlow logo and white text. The for-the-badge style produces the large, prominent badge format most common in GitHub profile READMEs. The tensorflow logo identifier pulls the official TensorFlow logo from Simple Icons, matching the logo used on tensorflow.org.

A badge alone tells visitors you know TensorFlow — but your repositories tell them how well you know it. The most compelling TensorFlow profile combines the badge with pinned repositories showing real ML projects: model training notebooks, inference pipelines, TensorFlow Serving deployments, or TFLite mobile integrations.

In your bio or about section, be specific about your TensorFlow experience level: distinguish between high-level Keras API usage, custom training loops with tf.GradientTape, model deployment with TF Serving, or mobile deployment with TFLite. Each of these represents a meaningfully different depth of knowledge. A 'TensorFlow developer' who has only used Sequential Keras models is different from one who has optimized custom CUDA kernels.

TensorFlow rarely appears alone in an ML stack. Effective badge groupings for ML engineers:

• Research stack: TensorFlow + Python + Jupyter + NumPy + Pandas
• Production ML stack: TensorFlow + Docker + Kubernetes + FastAPI + GCP
• Computer vision stack: TensorFlow + OpenCV + Python + CUDA
• NLP stack: TensorFlow + HuggingFace + Python + FastAPI

Organize ML badges in their own section labeled 'Machine Learning' or 'AI/ML Stack' rather than mixing them into a general programming languages section. The visual grouping helps visitors who scan for specific capability areas.

The github-readme-stats top languages card will show Python as your dominant language if TensorFlow is your primary framework — Python is the natural representation of TensorFlow work in GitHub repositories. For ML engineers whose TensorFlow models are part of larger applications, consider adding Jupyter Notebook or CUDA to your language card's hide list if they skew the percentage display.

For a complete ML developer profile, pin 2-3 repositories that demonstrate different aspects of TensorFlow usage: one showing model architecture and training, one showing deployment or serving, and one showing a complete end-to-end project. This portfolio approach is more convincing than listing TensorFlow in a tech stack without evidence.