Hussein Lezzaik HusseinLezzaik

To remove a submodule you need to:

Delete the relevant section from the .gitmodules file.
Stage the .gitmodules changes git add .gitmodules
Delete the relevant section from .git/config.
Run git rm --cached path_to_submodule (no trailing slash).
Run rm -rf .git/modules/path_to_submodule (no trailing slash).
Commit git commit -m "Removed submodule "
Delete the now untracked submodule files rm -rf path_to_submodule

do androids dream of cooking?

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.

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

General rules

Follow standard conventions.
Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
Boy scout rule. Leave the campground cleaner than you found it.
Always find root cause. Always look for the root cause of a problem.

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	;SMBDIS.ASM - A COMPREHENSIVE SUPER MARIO BROS. DISASSEMBLY
	;by doppelganger ([email protected])

	;This file is provided for your own use as-is. It will require the character rom data
	;and an iNES file header to get it to work.

	;There are so many people I have to thank for this, that taking all the credit for
	;myself would be an unforgivable act of arrogance. Without their help this would
	;probably not be possible. So I thank all the peeps in the nesdev scene whose insight into
	;the 6502 and the NES helped me learn how it works (you guys know who you are, there's no

	-- Two dashes start a one-line comment.

	--[[
	Adding two ['s and ]'s makes it a
	multi-line comment.
	--]]

	----------------------------------------------------
	-- 1. Variables and flow control.
	----------------------------------------------------

	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	""" 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

	# This code doesn't work, and isn't intended to.
	# The goal of this code is to explain how attention mechansisms work, in code.
	# It is deliberately not vectorized to make it clearer.

	def attention(self, X_in:List[Tensor]):
	# For every token transform previous layer's out
	for i in range(self.sequence_length):
	query[i] = self.Q * X_in[i]
	key[i] = self.K * X_in[i]
	value[i] = self.V * X_in[i]

Hussein Lezzaik HusseinLezzaik

do androids dream of cooking?

General rules

Design rules