TensorFlow 201: A slightly advanced tutorial
You wanted to build deep learning networks. You discovered TensorFlowlibrary through a Google search or Udacity course and decided to try it out, as you like Python. You looked through the sample pieces of code, recognized digits and tried to optimize it. It was exhilarating!
So, now you are ready to use it in a real problem at work, home or Kaggle. As you start writing your code from scratch, things start sounding more difficult than that tutorial led on. What now?
If that is where you find yourself, this article is for you. Though this is not meant to be full tutorial end to end, it will give you some pointers on how to make things work more easily in the world of TensorFlow.
Key topics covered in this tutorial are:
- Computational Model Basics, including mean scaling, and graph computation model
- Organizing and reusing code, including reusing variables and writing reusable code for creating layers
- Visualizing network with TensorBoard