TensorFlow-Tutorials

This repository is a collection of beginner-friendly tutorials for TensorFlow, a popular library used to build and train machine learning models. Each tutorial focuses on a single topic and is paired with a YouTube video, so you can read through the code or watch it explained. The tutorials are written as Jupyter Notebooks, which means they combine explanatory text, runnable code, and output in a single document you can open in a browser.

The topics covered span a wide range of machine learning techniques. Some tutorials introduce basic ideas like fitting a simple mathematical model to data or recognizing handwritten digits. Others go further into image recognition, natural language processing (understanding and generating text), machine translation, and reinforcement learning, which is a technique where a program learns by trial and error to achieve a goal. There is also a tutorial on time-series prediction, useful for forecasting data that changes over time.

The collection is split into two groups. The main set of tutorials has been updated to work with TensorFlow 2, which is the current version of the library. A second group of older tutorials only works with TensorFlow 1 and requires installing that older version separately. The author notes these older tutorials have not been converted because of the effort involved.

Each notebook can be run directly on Google Colab, which is a free online environment that does not require installing anything on your computer. This makes it straightforward to try the code without setting up a local environment.

The tutorials have also been translated into Chinese by the community. The author invites contributions for additional translations of the remaining tutorials.