Learn TensorFlow from scratch by running and modifying beginner-friendly code examples in your browser.
Follow step-by-step notebooks to understand how neural networks are built and trained.
Use the structured tutorials as a hands-on reference when building your first machine learning project.
TensorFlow-Course is a collection of beginner-friendly tutorials for learning TensorFlow, an open-source machine learning library originally developed by Google. The tutorials are designed to be simple, well-documented, and ready to run, addressing a common complaint about existing TensorFlow resources being either too complex or poorly explained. TensorFlow is a software framework used for building and training machine learning models, particularly neural networks (computational systems loosely inspired by the human brain, used for tasks like image recognition, language processing, and prediction). It uses dataflow programming, a model where computations are described as a graph of operations that data flows through. The course is structured as Jupyter Notebooks, interactive documents that combine text, code, and output in one place, letting you run and modify code step by step in your browser. Tutorials are organized into categories and cover topics from a basic "welcome" introduction up through more complex deep learning techniques. Each tutorial comes with source code and most include accompanying documentation. You would use this repository if you are a beginner or intermediate developer wanting to learn TensorFlow with clear, practical examples rather than dense documentation. The tutorials were updated to TensorFlow 2.3. It is written in Python and uses the Jupyter Notebook format, making it easy to follow along interactively. The full README is longer than what was provided.
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