Kory Mathewson korymath
grantslatton
/ hngen.py
Last active
September 27, 2021 11:07
A program that uses Markov chains to generate probabilistic Hacker News titles.
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| import urllib2 | |
| import re | |
| import sys | |
| from collections import defaultdict | |
| from random import random | |
| """ | |
| PLEASE DO NOT RUN THIS QUOTED CODE FOR THE SAKE OF daemonology's SERVER, IT IS | |
| NOT MY SERVER AND I FEEL BAD FOR ABUSING IT. JUST GET THE RESULTS OF THE | |
| CRAWL HERE: http://pastebin.com/raw.php?i=nqpsnTtW AND SAVE THEM TO "archive.txt" |
kevin-smets
/ iterm2-solarized.md
Last active
February 18, 2026 10:36
iTerm2 + Oh My Zsh + Solarized color scheme + Source Code Pro Powerline + Font Awesome + [Powerlevel10k] - (macOS)
tsiege
/ The Technical Interview Cheat Sheet.md
Last active
February 8, 2026 17:08
This is my technical interview cheat sheet. Feel free to fork it or do whatever you want with it. PLEASE let me know if there are any errors or if anything crucial is missing. I will add more links soon.
I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!
\
woollsta
/ chunkify.js
Last active
December 25, 2023 10:45
Fixes an issue with Google Chrome Speech Synthesis where long texts pause mid-speaking. The function takes in a speechUtterance object and intelligently chunks it into smaller blocks of text that are stringed together one after the other. Basically, you can play any length of text. See http://stackoverflow.com/questions/21947730/chrome-speech-sy…
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| /** | |
| * Chunkify | |
| * Google Chrome Speech Synthesis Chunking Pattern | |
| * Fixes inconsistencies with speaking long texts in speechUtterance objects | |
| * Licensed under the MIT License | |
| * | |
| * Peter Woolley and Brett Zamir | |
| */ | |
| var speechUtteranceChunker = function (utt, settings, callback) { |
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| Afghanistan | |
| Albania | |
| Algeria | |
| Andorra | |
| Angola | |
| Antigua & Deps | |
| Argentina | |
| Armenia | |
| Australia | |
| Austria |
The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.
The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.
kukuruza
/ gist_cifar10_train.py
Last active
November 4, 2024 17:36
Tensorflow: visualize convolutional filters (conv1) in Cifar10 model
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| from math import sqrt | |
| def put_kernels_on_grid (kernel, pad = 1): | |
| '''Visualize conv. filters as an image (mostly for the 1st layer). | |
| Arranges filters into a grid, with some paddings between adjacent filters. | |
| Args: | |
| kernel: tensor of shape [Y, X, NumChannels, NumKernels] | |
| pad: number of black pixels around each filter (between them) |
danijar
/ blog_tensorflow_sequence_classification.py
Last active
December 24, 2021 03:53
TensorFlow Sequence Classification
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| # Example for my blog post at: | |
| # https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/ | |
| import functools | |
| import sets | |
| import tensorflow as tf | |
| def lazy_property(function): | |
| attribute = '_' + function.__name__ |
A basic_rl.py provides a simple implementation of SARSA/Q-learning algorithms (specified by -a flag) with epsilon-greedy/softmax policies (specified by -p flag). You can also select the environment other than Roulette-v0 using -e flag. It also generates a graphical summary of your simulation.
Type the following command in your console to run the simulation using the default setting.
chmod +x basic_rl.py
./basic_rl.py
karpathy
/ pg-pong.py
Created
May 30, 2016 22:50
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
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| """ 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 |
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