📡 Organized & Useful Resources about Deep Learning with TensorFlow
The purpose of this project is to introduce a shortcut to developers and researcher for finding useful resources about TensorFlow.
There are different motivations for this open source project.
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.
There other similar repositories similar to this repository and are very comprehensive and useful and to be honest they made me ponder if there is a necessity for this repository! A great example is awesome-tensorflow repository which is a curated list of different TensorFlow resources.
The point of this repository is that the resources are being targeted. The organization of the resources is such that the user can easily find the things he/she is looking for. We divided the resources to a large number of categories that in the beginning one may have a headache!!! However, if someone knows what is being located, it is very easy to find the most related resources. Even if someone doesn't know what to look for, in the beginning, the general resources have been provided.
The written and visual resources have been split. Moreover, As one can search in the documentation, the number of categories might look to be too much. For finding the most relevant resources, please at first look through the general resources.
In this section, different TensorFlow topics and their associated resources will be addressed.
First of all, the TensorFlow must be installed!
This part points to resources on how to start to code with TensorFLow
Advanced machine learning users can go deeper in TensorFlow in order to hit the root. Scratching the surface may never take us too further!
The references here, deal with the details of programming and writing TensorFlow code.
The first part is always how to prepare data and how to provide the pipeline to feed it to TensorFlow. Usually providing the input pipeline can be complicated, even more than the structure design!
Variables are supposed to hold the parameters and supersede by new values as the parameters are updated. Variables must be clearly set and initialized.
Different utilities empower TensorFlow for faster computation in a more monitored manner.
This section is dedicated to provide tutorial resources on the implementation of different models with TensorFlow.
This section is dedicated to provide resources that are mainly open source projects developed by TensorFlow. Those might be comprehensive tutorials on working example.
This section is dedicated to provide published resources on TensorFlow, Such as websites, blogs, and books.
For typos, please do not create a pull request. Instead, declare them in issues or email the repository owner. Please note we have a code of conduct, please follow it in all your interactions with the project.
Please consider the following criterions in order to help us in a better way:
We are looking forward to your kind feedback. Please help us to improve this open source project and make our work better. For contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate your kind feedback and support.Опубликовать в Twitter Опубликовать в Facebook