Haard's repositories
Script-Generation
Generating movie scripts by genre using CTRL framework and GPT-2
GeneratePixelArt-GANs
Learning to train GANs
HabitatAI-RL
Create an agent to navigate to a point goal with noisy actuation/sensing and no GPS/compass (for 2020 HabitatAI pointNav challenge)
ProductivityApp
Python Tkinter GUI App to keep track to tasks to improve productivity
Impressionist
Netflix Karaoke - Chrome Extension
ControlDeliveryPowergrid-Simulations
Code used to perform simulations for CDG project
DCGAN-tensorflow
A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"
DSA-projects
Airport queue simulation, parse tree for arithmetic expression and Dijkstra's shortest path
habitat-api
A modular high-level library to train embodied AI agents across a variety of tasks, environments, and simulators.
icml2016
Generative Adversarial Text-to-Image Synthesis
jalammar.github.io
Build a Jekyll blog in minutes, without touching the command line.
learning-react
Following tic-tac-toe tutorial on https://reactjs.org/tutorial/tutorial.html
Minds-and-Machines
Inspired thoughts from assignments, debates and presentations in philosophy class
Miscellaneous-R-Code
Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms.
mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
ML-CourseProjects
Machine Learning Course Projects
ML_Resources
List of and/or notes about resources I've used often in ML
mysqldump-to-csv
A quickly-hacked-together Python script to turn mysqldump files to CSV files. Optimized for Wikipedia database dumps.
sgan
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
Text-to-Image-Synthesis
Pytorch implementation of Generative Adversarial Text-to-Image Synthesis paper
tube_dl
Youtube video downloader and info extractor for python.
WordCount
Implementing the wc command with and additional functionality