**For assignment0_05 please refer to the assignment
[folder](TBD)**

NN from scratch:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/girafe-ai/ml-course/blob/22f_basic/week0_07_intro_to_DL/week07_nn_from_scratch_practice_edition.ipynb)

NN from scratch(full):
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/girafe-ai/ml-course/blob/22f_basic/week0_07_intro_to_DL/week07_nn_from_scratch.ipynb)

**Further readings**:

- [en] Notes on vector and matrix derivatives:
  http://cs231n.stanford.edu/vecDerivs.pdf
- [en] Stanford notes on backpropagation:
  http://cs231n.github.io/optimization-2/
- [en] Stanford notes on different activation functions (and just intuition):
  http://cs231n.github.io/neural-networks-1/
- [en] Great post on Medium by Andrej Karpathy:
  https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b
- [en] CS231n notes on data preparation (batch normalization over there):
  http://cs231n.github.io/neural-networks-2/
- [en] CS231n notes on gradient methods:
  http://cs231n.github.io/neural-networks-3/
- [en] Original paper introducing Batch Normalization:
  https://arxiv.org/pdf/1502.03167.pdf
- [en] What Every Computer Scientist Should Know About Floating-Point
  Arithmetic: https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html
