Skip to content

Instantly share code, notes, and snippets.

View JanhaviDadhania's full-sized avatar

Janhavi Dadhania JanhaviDadhania

View GitHub Profile
@NTag
NTag / remoteAtom.md
Last active December 19, 2023 12:09
How to install and use remote-atom

Installation

  1. Install Atom on your computer (https://atom.io)

  2. Launch Atom, go in Preferences, then chose Install, search for "remote-atom", and install it

  3. On the distant machine:

    cd ~
    wget https://raw.githubusercontent.com/aurora/rmate/master/rmate
    chmod +x rmate
@karpathy
karpathy / pg-pong.py
Created May 30, 2016 22:50
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward
@diogofurtado
diogofurtado / minix_tutorial.md
Last active April 21, 2024 20:04 — forked from Drowze/minix_tutorial.md
MINIX: FROM ZERO TO ZERO+1: From a default installation of Minix (version 3.3.0) to a SSH capable, source included Minix.

Disclaimer

I take no responsability for any problems a user might have on following this gist. This includes university problems.

The motivation for this is to document, as dummy-oriented as possible, a way to setup and add a system call to Minix OS. This is a classic assignment at Operational Systems classes (and is pretty cool tbh)

Configuration:

ISO used: minix_R3.3.0-588a35b.iso

@harshraj22
harshraj22 / IdeaList.md
Last active August 13, 2021 11:16
New ideas for BTP: Visual Question Answering
  • 1x1 convolutions have been extensively used to reduce the number of parameters without affecting the results much
  • Deep Mutual Learning: Unlike bagging/ boosting, models learn jointly, and help each other to fit well
  • Skip connections: Help solving degradation problem without adding parameters.
  • Hard Sample Mining