Name: Bibek Bhattarai
Type: User
Company: The George Washington University
Bio: PhD candidate at George Washington University. My area of interests are Graph algorithms, Parallel Computing, Computer Security, and Machine Learning
Location: Washington, DC 20052
Blog: https://bbhattarai.com
Bibek Bhattarai's Projects
Various public documents, whitepapers and articles about APT campaigns
A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.
Curated list of awesome cybersecurity companies and solutions.
🔗 All the resources I could find for learning Ethical Hacking and Penetration Testing.
A curated list of tools for incident response
:octocat: Machine Learning for Cyber Security
a curated list of awesome streaming frameworks, applications, etc
Config files for my GitHub profile.
black hat python
Bluehat 2018 Graphs for Security Workshop
Control Flow Integrity (CFI) enforcement programs
FPGA prototyping of pipe-lined Cordic Processor
The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++
Python library assists deep learning on graphs
Detect Tactics, Techniques & Combat Threats
LaTeX files for the Deep Learning book notation
Tensorflow tutorial of building different dynamic recurrent neural network
Set of EVTX samples (>170) mapped to MITRE Att@k tactic and techniques to measure your SIEM coverage or developed new use cases.
Creates a .csv file of all players in the English Player League with their respective team and total fantasy points
FTPipe and related research pipeline model parallelism research as described in the paper
gStore v2.0 - a graph based RDF triple store.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
My 100 Days of ML Code Challenge Repository. Note: I stopped updating in the blogger site. The days after 100 days are documented in another repository.
Resources to learn ML and DL
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.