Thomas M (@thomasxm)

Top repositories

1

BOAZ_beta

Multilayered AV/EDR Evasion Framework
C++
103
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2

Akira-obfuscator

Another LLVM-obfuscator based on LLVM-17. A fork of Arkari
47
star
3

BOAZ

A Multilayered AV/EDR Evasion Framework and AV Testing Tool.
5
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4

CNN-Dog-Human-detection-and-dogbreed-classification

In this project I will build a convolutional network that could detect human and dog images, and then detect and resembling the breeds of dog. User supplied images are allowed.
Jupyter Notebook
4
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5

Clefia_cipher_python_implementation_for_general_testing

Comparison tests on lightweight Clefia cipher proposed by SONY
Python
2
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6

Project-tv-script-generation-RNN

This project implemented RNN and embedding to generates tv scripts based on the famous "Seinfeld" script from 9 seasons
Jupyter Notebook
2
star
7

hello-world

Tutorial repository, fun starts
1
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8

project-face-generation-with-DCGAN

In this project, we use deep convolutional generative adversarial networks to generate new images of faces.
HTML
1
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9

Project-bike-sharing-rental

In this project, a neural network is built to carry out prediction problem on a real dataset for bike sharing problem over two years of period.
Jupyter Notebook
1
star
10

Deploying-a-Sentiment-Analysis-Model

In this project you will construct a recurrent neural network for the purpose of determining the sentiment of a movie review using the IMDB data set. You will create this model using Amazon's SageMaker service. In addition, you will deploy your model and construct a simple web app which will interact with the deployed model.
Jupyter Notebook
1
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11

Simple-neural-network-and-linear-regression-analysis-on-avocado-data-in-US

In this project we will concentrate on the time series forecasting analysis of avocado prices over 4 years period within the US region. The analysis is performed with simple linear regression model and one hidden layer neural network to demonstrate the different aspects of the existing data, and highlighted the area of improvements need to be done. The LR and NN are implemented from scratch without using any high level packages like Pytorch and Tensorflow in order to demonstrate the implementing processes.
Jupyter Notebook
1
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