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"""
Classic cart-pole system implemented by Rich Sutton et al.
Copied from http://incompleteideas.net/sutton/book/code/pole.c
permalink: https://perma.cc/C9ZM-652R

Continuous version by Ian Danforth
"""

import math
import gym

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Local JavaScript -> Hosted Jupyter notebook

Description

If you have:

• A hosted Jupyter notebook (e.g. on Google Colaboratory)
• A local JavaScript development environment you prefer to use

You can:

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If you see this error when trying to import pandas under osx 10.14 Mojave

ModuleNotFoundError: No module named '_lzma'

You may be missing the xz libraries (https://tukaani.org/xz/)

This may be corrected by using homebrew to install it.

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#############################################################################
# Full Imports

from __future__ import division
import math
import random

"""
This is a pure Python implementation of the K-means Clustering algorithmn. The
original can be found here:

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User

Play the role of Gatsby from the great Gatsby. I'm confused about the meaning of the green light you're always looking at.

ChatGPT

Ah, old sport, you've noticed the green light, have you? That small, mysterious beacon across the bay. It's true, I've spent 
many nights gazing at it, lost in thought. You see, that green light represents my hopes, my dreams, and the longing I have 
for the life I always wanted.

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Title: Do Language Models Need Sensory Grounding for Meaning and Understanding?

Host: Raphaël Millière

Raphaël Millière: Good evening, and welcome to this debate on the Philosophy of Deep Learning. Today, we have six
distinguished panelists who will discuss whether language models need sensory grounding for meaning and understanding. I'd
like to remind everyone to be respectful, thoughtful, and don't hesitate to add a touch of humor. Let's begin with Yann LeCun.


Yann LeCun: Thank you, Raphaël. It's an honor to be here. I think that grounding language models in sensory data is

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# This script replaces all strings matching https://t.co/ links in a the manifest with their expanded versions
import json
import re
import urlexpander

# Open the file containing twitter bookmarks in json
# and convert to a dictionary
with open('bookmark-manifest.json', 'r') as fh:
    contents = fh.read()
    bookmarks = json.loads(contents)

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How to capture WebGL/Canvas by piping data over a websocket.

This Gist builds on https://gist.github.com/unconed/4370822 from @unconed.

Instead of the original method which writes to the browsers sandboxed filesystem here we use a websocket connection provided by websocketd to pipe image data to a short python script that writes out the .png files to disk.

Install websocketd

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%%javascript

Jupyter.keyboard_manager.command_shortcuts.remove_shortcut('up');
Jupyter.keyboard_manager.command_shortcuts.remove_shortcut('down');

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"""
Classic cart-pole system.

Pymunk version by Ian Danforth
"""

import math
import gym
import pygame
import pymunk