| import patty | |
| import strformat | |
| import strutils | |
| import sugar | |
| include karax / prelude | |
| include karax / kdom | |
| include karax / kajax | |
| type |
| """ | |
| nim_magic.py | |
| Jupyter cell magic for your favorite programming language. | |
| This is now also available as a proper repo: | |
| https://github.com/apahl/nim_magic | |
| All further changes will be tracked there. | |
| Requirements: Nim (https://nim-lang.org), nimpy (`nimble install nimpy`, thanks to @yglukhov for this great library!) |
| #! /usr/bin/env ruby | |
| # NOTE: Requires Ruby 2.1 or greater. | |
| # This script can be used to parse and dump the information from | |
| # the 'html/contact_info.htm' file in a Facebook user data ZIP download. | |
| # | |
| # It prints all cell phone call + SMS message + MMS records, plus a summary of each. | |
| # | |
| # It also dumps all of the records into CSV files inside a 'CSV' folder, that is created |
Recently I stumbled upon a post which takes a closer look at the yes command line tool. The main purpose of it is to write endless stream of a single letter y at a ridiculous speed.
On the first glance this seems like a really simple problem, just two lines of Nim and you're done, right?
while true:
echo "y"And indeed, this gives us plenty of y's. But when we take a look at the write speed..
I upgraded my iPhone 5s to iOS 10 and could no longer retrieve photos from it. This was unacceptable for me so I worked at achieving retrieving my photos. This document is my story (on Ubuntu 16.04).
The solution is to compile libimobiledevice and ifuse from source.
Who is this guide intended for?
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.
| """ | |
| 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): |
A lot of math grad school is reading books and papers and trying to understand what's going on. The difficulty is that reading math is not like reading a mystery thriller, and it's not even like reading a history book or a New York Times article.
The main issue is that, by the time you get to the frontiers of math, the words to describe the concepts don't really exist yet. Communicating these ideas is a bit like trying to explain a vacuum cleaner to someone who has never seen one, except you're only allowed to use words that are four letters long or shorter.
What can you say?
| /* | |
| Copyright 2012-2021 Viktor Klang | |
| Licensed under the Apache License, Version 2.0 (the "License"); | |
| you may not use this file except in compliance with the License. | |
| You may obtain a copy of the License at | |
| http://www.apache.org/licenses/LICENSE-2.0 | |
| Unless required by applicable law or agreed to in writing, software |