Ned Ruggeri ruggeri

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This is my song.

Heaps are an implementation of the priority queue abstract data type:

insert(entry, priority).
- priority is an integer priority. Entries with "higher priority" will be extracted before entries with "lower priority."
Lower number often is "higher priority." This makes sense

	def insertion_sort(array)
	i = 0
	while i < array.length
	j = 0
	while array[j] < array[i]
	j += 1
	end

	# array[j] is the first element >= array[i]
	# delete array[i] and move it to just before position j

	import tensorflow as tf

	# Notice that x and y have the same initial value, but y_var has
	# totally different scale from x_var. Therefore y_var has much farther
	# to change than x_var.
	x_var = tf.Variable(1.0)
	y_var = tf.Variable(100.0)
	y_var_scaled = y_var / 100.0

	# This is just a silly linear equation. It will have a derivative of 2

	import keras.backend as K
	from keras.datasets import mnist
	from keras.layers import Dense, Flatten, Input, Reshape
	from keras.models import Model
	from keras.optimizers import Adam
	import numpy as np

	# == This code trains a denoising autoencoder. ==
	input_tensor = Input(shape = (28, 28))
	flattened_input = Flatten()(input_tensor)

	# Resource: https://keras.io/getting-started/functional-api-guide/

	import keras.backend as K
	from keras.datasets import mnist
	from keras.layers import Dense, Input, Lambda
	from keras.models import Model
	from keras.optimizers import Adam
	from keras.utils import to_categorical
	import numpy as np

	promise.then((result) => {
	// promise resolved successfully.
	console.log(`Result is ${result}`);
	}, (error) => {
	// promise was failed
	console.log(`Error is ${error}`);
	});

	// Also: promise.then(null, (error) => { ... }) is the same as promise.catch((error) => { ... }).
	// The Promise#catch method is just a convenience. You can always just pass a null success handler to then.

	#!/usr/bin/env bash

	gcc -std=c11 -mavx -mavx2 convolution_test.c -o convolution_test

	from keras.datasets import imdb
	import numpy as np

	TOP_N_WORDS = 1_000
	(x_train, y_train), (x_test, y_test) = imdb.load_data(
	num_words = TOP_N_WORDS
	)

	# Transform dataset from variable-length word sequences to a
	# binary valued dense matrix.