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This repository is aimed to provide simple and ready-to-use tutorials for TensorFlow. The explanations are present in the current page which is the wiki for this repository. Each tutorial has a source code and its documentation.
There are different motivations for this repository. Some are TensorFlow-related which is one of the bests up to the moment that this document is being written! The question is why this repository has been created among all other available tutorials on the web?
A deep learning is of great interest these days, the crucial necessity for rapid and optimized implementation of the algorithms and designing architectures is the software environment. TensorFlow is designed to facilitate this goal. The strong advantage of TensorFlow is it flexibility is designing highly modular model which also can be a disadvantage too for beginners since lots of the pieces must be considered together for creating the model. This issue has been facilitated as well by developing high-level APIs such as Keras and Slim which gather lots of the design puzzle pieces. The interesting point about TensorFlow is that its trace can be found anywhere these days. Lots of the researchers and developers are using it and its community is growing with the speed of light! So the possible issues can be overcame easily since they might be the issues of lots of other people considering a large number of people involved in TensorFlow community.
Developing open source project for the sake of just developing something is not the reason behind for this effort. Considering the 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 usually happens to most of the open source projects, but why and how?
First of all, what's the point of putting effort on 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 spending time for 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 implementation to provide better insight about how to use TensorFlow fast and efficient. It is worth noting that, the main goal of this project is providing well-documented tutorials and less-complicated codes!
The tutorials in this repository are partitioned into relevant categories.
| # | topic | Source Code | |
|---|---|---|---|
| 1 | Start-up | Welcome / IPython | Documentation |
| 2 | TensorFLow Basics | Basic Math Operations / IPython | Documentation |
| 3 | TensorFLow Basics | TensorFlow Variables / IPython | Documentation |
| 4 | Machine Learning | Linear Regression / IPython | Documentation |
| 5 | Machine Learning | Logistic Regression / IPython | Documentation |
| 6 | Machine Learning | Linear SVM / IPython | |
| 7 | Machine Learning | MultiClass Kernel SVM / IPython | |
| 8 | Neural Networks | Multi Layer Perceptron / IPython | |
| 9 | Neural Networks | Convolutional Neural Networks | Documentation |
| 10 | Neural Networks | Undercomplete Autoencoder |