At IUST, we have a bunch of courses around Deep Learning. Considering the goal for these courses, which is applied Deep Learning, the Keras framework was the primary choice to be taught in classes. In fact, Keras, due to its high-level and easy APIs, is an excellent fit for this use-case, which is verified by the feedback we had from students. However, as many students went further from the class’s scope and became interested in the research direction, they needed more flexibility. Please don’t get me wrong; Keras is a great framework, which can actually be useful in research but it may not be as productive as we want.
With the introduction of TensorFlow 2.0, we decided to replace Keras with TensorFlow. TensorFlow 2.0 brings the maturity of Keras modeling APIs accompanied by the flexibility and end-to-end ecosystem of tools that were created around TensorFlow 1. This combination of features makes the TensorFlow 2.0 a great choice for both production and research, especially as Eager Execution (Dynamic Graph) is enabled by default.
Therefore, we created a set tutorial for TensorFlow 2.0 to be taught in our classes, something similar to Stanford CS 20, but more compact and more up-to-date.
Header Photo from TF dev summit 19
Let me answer this question using the contents from the official page:
First, we aim to approach TensorFlow 2.0 from a practical point of view so that you can start using the framework as soon as possible. Secondly, we try to include and use a wide range of information such as TF team talks, API references, and practical experiences in the making of these materials to make it as comprehensive as possible. Thus, the user can enjoy an all-in-one package. Finally, tutorials are organized in a simple-to-complex manner that can benefit many newcomers. Moreover, the assignments that come with these tutorials make them a ready-to-use option in use in special (short) courses.
These tutorials are currently “under construction,” and we are completing them. Therefore, the list of sections can be changed in the course of development. However, here are the parts that currently, we plan to cover:
We believe that tutorials, similar to software projects, can also benefit from the open-source model. Therefore, we plan to manage this project using community opinions. If you think the tutorial can be made better by including particular content, you are more than welcome to share it with us and include it in GitHub. Additionally, we have prepared a bunch of templates to hasten the contribution and the creation of excellent materials.
You can access all materials here: https://github.com/iust-deep-learning/tensorflow-2-tutorial
I hope you find this set of tutorials useful. If you have any questions or feedback, the comment section is at your disposal.