Grant Stenger (@GrantStenger)
  • Stars
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    152
  • Global Rank 152,276 (Top 6 %)
  • Followers 133
  • Following 488
  • Registered over 8 years ago
  • Most used languages
    Python
    41.7 %
    C++
    8.3 %
    HTML
    8.3 %
    Solidity
    8.3 %

Top repositories

1

n3rp

N3RP (the NFT Rental Protocol) allows users to trustlessly rent out their ERC721-based assets.
Jupyter Notebook
55
star
2

rl-finance

CAIS++ Spring 2019 Project: Building an Agent to Trade with Reinforcement Learning
Jupyter Notebook
40
star
3

SRGAN-TreeHacks

TreeHacks 2019: Best computer vision prize and IBM’s #1 favorite health hack. Upscaling low resolution videos with a novel super resolution GAN architecture.
Python
14
star
4

stock-price-analysis

This is our final project for our data analytics bootcamp. We will be analyzing stock price data (mostly large-cap U.S. equities). Predicting financial data is a famously difficult problem, so this is mostly an exercise in using some of the tools we've learned this year.
HTML
12
star
5

SoulboundNFTs

A Standard for Non-Transferable NFTs
Solidity
11
star
6

ai-for-adversarial-games

Building classical minimax + alpha-beta pruning algorithms (with hardcoded heuristics for extremely complex game spaces), and beating them with modern reinforcement learning techniques.
Python
7
star
7

2048-Reinforcement-Learning

AI that uses Deep Q-Learning to play 2048. Also playable in console for humans.
Python
6
star
8

0xPlace

Decentralized r/place
2
star
9

Apollo-text-parser

Parses cognitive behavior therapy transcripts for data that will later be used to build an AI chatbot to help with mental health on college campuses.
C++
1
star
10

minimax-connect4

Quick minimax algorithm to play connect4. Practice before trying to build a chess ai.
Python
1
star
11

course-optimizer

Optimal course planner: Network flows and combinatorial optimization, Web scraping, Full-stack web app.
Python
1
star
12

intro-to-deep-learning-lesson

Jupyter Notebook
1
star
13

trump-obama-tweet-classifier

Uses recurrent neural networks (specifically Long Short-Term Memory networks) to classify tweets from either Obama or Trump with 95% accuracy.
Jupyter Notebook
1
star