book

• Dynamic variable naming.
• Internal variables.
• Turn the non-functions into functions.
  • This is so tests can be written.
  • It also shows a working use case for the task.
• Add a CONTRIBUTING.md.
• Look into side effects of using shopt and set.
• /dev/tcp
• Convert to pdf
  • Add a cover.
  • Add references.
• Write some

Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.

It can be clearly seen in chapter 6([CNN Lenet](ht