rene/ma_cisco_malware
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René Knaebel 8b17bd0701 add TSNE embedding; server evaluation visualization
2017-10-19 17:39:37 +02:00
data
refactoring and adding standard files for .gitignore and .keep in data
2017-06-30 09:04:24 +02:00
models
refactor server training into separate file; add additional info to hyperband log
2017-10-19 17:37:29 +02:00
scripts
remove model selection based on validation loss
2017-09-16 15:25:34 +02:00
.gitignore
add visualization for training curves, pr, roc
2017-07-14 14:58:17 +02:00
arguments.py
add bulk embedding visualization and deep1 network
2017-10-09 14:19:01 +02:00
dataset.py
add TSNE embedding; server evaluation visualization
2017-10-19 17:39:37 +02:00
fancy.sh
add TSNE embedding; server evaluation visualization
2017-10-19 17:39:37 +02:00
hyperband.py
refactor server training into separate file; add additional info to hyperband log
2017-10-19 17:37:29 +02:00
main.py
add TSNE embedding; server evaluation visualization
2017-10-19 17:39:37 +02:00
Makefile
change hyperband to count minimal val_loss over all losses
2017-10-05 12:55:46 +02:00
README.md
Init repo
2017-06-27 20:01:09 +02:00
rerun_models.sh
refactor hyperband implementation
2017-09-29 22:59:57 +02:00
run_model_rene.sh
add first version of model averaging visualization
2017-09-26 19:25:37 +02:00
run_model.sh
add first version of model averaging visualization
2017-09-26 19:25:37 +02:00
run.sh
add first version of model averaging visualization
2017-09-26 19:25:37 +02:00
server.py
refactor server training into separate file; add additional info to hyperband log
2017-10-19 17:37:29 +02:00
test.sh
add parser argument for naming in multi model modes, minor fixes, re-set fix vals for network - need to make them flexible
2017-09-05 17:40:57 +02:00
utils.py
add bulk embedding visualization and deep1 network
2017-10-09 14:19:01 +02:00
visualize.py
add TSNE embedding; server evaluation visualization
2017-10-19 17:39:37 +02:00

README.md

This is the init commit for my master thesis project.

Description
My master thesis project on malware detection using neural networks and multi task learning
Readme 385 KiB
Languages
Python 99.7%
Makefile 0.3%