Dhawal Arora awhitesong

Git cheat list

name of the current banch and nothing else (for automation)
```
git rev-parse --abbrev-ref HEAD
```
all commits that your branch have that are not yet in master
```
git log master..<HERE_COMES_YOUR_BRANCH_NAME>
```

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

Django documentation says to use:

WSGIScriptAlias / /path/to/mysite.com/mysite/wsgi.py
WSGIPythonPath /path/to/mysite.com

<Directory /path/to/mysite.com/mysite>
<Files wsgi.py>
Require all granted

This document has now been incorporated into the uWSGI documentation:

http://uwsgi-docs.readthedocs.org/en/latest/tutorials/Django_and_nginx.html

Set up Django, nginx and uwsgi

Steps with explanations to set up a server using:

	"""
	Open a random CVPR talk (oral or spotlight) in the browser
	"""

	import urllib.request, urllib.error
	import webbrowser
	import random

	URL = "http://cvpr2020.thecvf.com/sites/default/files/2020-03/accepted_list_0.txt"

	# Instructions for 4.14 and cuda 9.1
	# If upgrading from 4.13 and cuda 9.0
	$ sudo apt-get purge --auto-remove libcud*
	$ sudo apt-get purge --auto-remove cuda*
	$ sudo apt-get purge --auto-remove nvidia*
	# also remove the container directory direcotory at /usr/local/cuda-9.0/

	# Important libs required with 4.14.x with Cuda 9.X
	$ sudo apt install libelf1 libelf-dev

	import discord
	import asyncio

	client = discord.Client()

	@client.event
	async def on_ready():
	print('Logged in as')
	print(client.user.name)
	print(client.user.id)

	'''This scripts implements Kim's paper "Convolutional Neural Networks for Sentence Classification"
	with a very small embedding size (20) than the commonly used values (100 - 300) as it gives better
	result with much less parameters.

	Run on GPU: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python imdb_cnn.py

	Get to 0.853 test accuracy after 5 epochs. 13s/epoch on Nvidia GTX980 GPU.
	'''

	from __future__ import print_function

	from lasagne.layers import InputLayer
	from lasagne.layers import DenseLayer
	from lasagne.layers import ConcatLayer
	from lasagne.layers import NonlinearityLayer
	from lasagne.layers import GlobalPoolLayer
	from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer
	from lasagne.layers.dnn import MaxPool2DDNNLayer as PoolLayerDNN
	from lasagne.layers import MaxPool2DLayer as PoolLayer
	from lasagne.layers import LocalResponseNormalization2DLayer as LRNLayer
	from lasagne.nonlinearities import softmax, linear

	/**
	* In this code we have a very large array called arr, and very large set of operations
	* Operation #1: Increment the elements within range [i, j] with value val
	* Operation #2: Get max element within range [i, j]
	* Build tree: build_tree(1, 0, N-1)
	* Update tree: update_tree(1, 0, N-1, i, j, value)
	* Query tree: query_tree(1, 0, N-1, i, j)
	* Actual space required by the tree = 2*2^ceil(log_2(n)) - 1
	*/