* Created cybersecurity notebook and downloaded data with wget

* Reorganized the cybersecurity notebook

* Read the entire dataset as a pytorch tensor and trained a simple MLP on the UNSW_NB15 dataset

* small changes to markdown

* Training and testing have the same integer encoding

* Added debugger tool

* added loss visualization

* aded 1hot encoder and separated dataloader

Added 1-hot encoder with scikit learn.
Seperated the dataloader into a python file.

* changes to get 99.998% accuracy

* changed loss plot

* accuracy at 70% after 50 epochs

However, loss is not ok

* got 75% accuracy

* see loss

* added scheduled statistics

* added iterator over all possible parameters

* updates on automation

debugging model error

* Delete cybersecurity-checkpoint.ipynb

* Delete cybersecurity_2-checkpoint.ipynb

* Delete exemplofixe-checkpoint.ipynb

* Delete UNSW_NB15_testing-set.csv

* Delete UNSW_NB15_train.csv

* Delete UNSW_NB15_training-set.csv

* Delete UNSW_NB15_val.csv

* general cleanup

* general cleanup

* updates with 75.7238% accuracy

* added quantization of the dataset

* debugging the quantization of the dataset

* added plots for the debugging

* debugging the quantization. Show the differences

* update debugging

* updates on debugging

* updates on debugging quantization

* updates on debug, uint32 df added

* updates on debugging

* added 2 pictures for the debugging

* Added quantization of the dataset

* aded results for training the model with the quantized dataset

* added quantization of the dataset

* added new notebook with model definition with brevitas

* cleaning up documents

* modified the model definition

* Changed the loss function

* Added debug with pdb

* Successfully created neural network with Brevitas

* Correctly quantize the dataset

* Added export of onnx model

* Added FINN validation of the Brevitas model

* improved dataloader

* general cleanup

* General Cleanup

* General Cleanup

* Completed verifying the FINN model against Brevitas

* Added new layer to MLP - Debugging

* verified that the MLP model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 test inputs

* verified that the MLP model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 test inputs

* verified that model with new layer (QuantIdentity) outputs the same in Brevitas and in FINN for all 82332 inputs

* verified that model with new layer and input shifted to accept {-1,1}, outputs the same in Brevitas (input is in {-1,+1}) and in FINN (input is in {-1,+1})  for the 82332 inputs

* General cleanup and added text

* General cleanup: improved text

* General cleanup: fixed text typos

* General cleanup: Added text

* Delete cybersecurity.ipynb

* Delete dataloader.py

* Rename cybersecurity_Brevitas_1bit.ipynb to 1-cybersecurity-Brevitas-1bit.ipynb

* Rename cybersecurity_Brevitas_Verification.ipynb to 2-cybersecurity-finn-verification.ipynb

* Added the last notebook

* Added last notebook describing the finn build

* Added changed parameters to see differences

* Added changed parameters and added text

* Fixed typo

* [Notebooks] reorganize into folders, add README for cybsec

* [Notebook] add license header and refs to cybsec dataset quantizer

* [Notebooks] rename cybsec notebook files

* [Notebook] first pass thru cybsec part 1

* [Notebook] refactor more of cybsec part 1

* [Notebook] add option to use pretrained weights

* fixed outline and typo

* [Notebook] update cybsec notebook #2 and gitignore

* [Notebook] start refactoring cybsec part 3

* [ConvertToHLS] allow out_scale=2 for bipolar MT

* [Build] add alternative set of steps for estimation only

* [Transform] attempt to handle padding for IODMAs

* [Transform] explicitly ignore IODMA nodes for InsertDWC

* [Notebook] full pass over cybsec notebook 3

* [Util] move vivado utils into finn-base

* fixed outline

* [HLSCustomOp] better err msg on ipgen failure

Co-authored-by: Alina Vasilciuc <alinav@xlnx.xilinx.com>
Co-authored-by: Yaman Umuroglu <yamanu@xilinx.com>