This repository aims to provide simple and ready-to-use tutorials for TensorFlow. There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Developing open source projects for the sake of just developing something is not the reason behind this effort. Considering a large number of tutorials that are being added to this large community, this repository has been created to break the jump-in and jump-out process that usually happens to most of the open source projects, but why and how? First of all, what's the point of putting effort into something that most of the people won't stop by and take a look? What's the point of creating something that does not help anyone in the developers and researchers community? Why spend time on something that can easily be forgotten? But how we try to do it? Even up to this very moment there are countless tutorials on TensorFlow whether on the model design or TensorFlow workflow. Most of them are too complicated or suffer from a lack of documentation. There are only a few available tutorials which are concise and well-structured and provide enough insight for their specific implemented models. The goal of this project is to help the community with structured tutorials and simple and optimized code implementations to provide better insight into how to use TensorFlow quick and effectively. It is worth noting that, the main goal of this project is to provide well-documented tutorials and less-complicated code!
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