ktavabi

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

ktavabi

HTML Tags You Can Use on GitHub

Wherever HTML is rendered on GitHub (gists, README files in repos, comments on issues and pull requests, ...) you can use any of the HTML elements that GitHub Flavored Markdown (GFM) provides syntactic sugar for. You can either use the syntactic sugar that GFM (or other GitHub-supported markup language you're using) provides or, since Markdown can contain raw HTML, you can enter the HTML tags manually.

But GitHub also allows you to use a few HTML elements beyond what Markdown provides by entering the tags manually, and some of them are styled with CSS. Most raw HTML tags get stripped before rendering the HTML. Those tags that can be generated by GFM syntactic sugar, plus a few more, are whitelisted. These aren't documented anywhere that I can find. Here's what I've discovered so far:

<details> and <summary>

A `<detai

ktavabi

People

:bowtie:	😄 :smile:	😆 :laughing:
😊 :blush:	😃 :smiley:	☺️ :relaxed:
😏 :smirk:	😍 :heart_eyes:	😘 :kissing_heart:
😚 :kissing_closed_eyes:	😳 :flushed:	😌 :relieved:
😆 :satisfied:	😁 :grin:	😉 :wink:
😜 :stuck_out_tongue_winking_eye:	😝 :stuck_out_tongue_closed_eyes:	😀 :grinning:
😗 :kissing:	😙 :kissing_smiling_eyes:	😛 :stuck_out_tongue:

ktavabi

#!/usr/bin/env bash
#
# Bootstrap script for setting up a new OSX machine
#
# This should be idempotent so it can be run multiple times.
#
# Some apps don't have a cask and so still need to be installed by hand. These
# include:
#
# - Twitter (app store)

ktavabi

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt

rng = np.random.RandomState(0)
M, n_sensors = 100, 1000
# Make "Fourier coefficients" here
data = rng.randn(M, n_sensors) + rng.randn(M, n_sensors) * 1j
data += 0.  # can be non-zero to test that it actually works for some signal
mean = np.mean(data, axis=0)

ktavabi

Keybase proof

I hereby claim:

• I am ktavabi on github.
• I am ktavabi (https://keybase.io/ktavabi) on keybase.
• I have a public key ASDfx1uYa6Pbzq6hMXPsi1cmRYHRnaFE53NxmAL83f9shgo

To claim this, I am signing this object:

ktavabi

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

ktavabi

# query nvidia card
lspci -vnn | grep -i VGA -A 12

# install nvidia driver 352.63
sudo apt-get install nvidia-352
sudo reboot

# Download OS-Arch CUDA Toolkit package from https://developer.nvidia.com/cuda-downloads
cd ~/Downloads
wget -O cuda.deb http://developer.download.nvidia.com/compute/cuda/7.5/Prod/local_installers/cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb

ktavabi

"""
=========================================================
circular data analysis functions
=========================================================

"""

# Authors : Anne Kosem and Alexandre Gramfort
# License : Simplified BSD