Łukasz Augustyniak laugustyniak

*2vec papers

act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
apk2vec https://arxiv.org/abs/1809.05693
app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
ast2vec https://arxiv.org/abs/2103.11614
attribute2vec https://arxiv.org/abs/2004.01375
author2vec http://dl.acm.org/citation.cfm?id=2889382
baller2vec https://arxiv.org/abs/2102.03291
bb2vec https://arxiv.org/abs/1809.09621

NLTK API to Stanford NLP Tools compiled on 2015-12-09

Stanford NER

With NLTK version 3.1 and Stanford NER tool 2015-12-09, it is possible to hack the StanfordNERTagger._stanford_jar to include other .jar files that are necessary for the new tagger.

First set up the environment variables as per instructed at https://github.com/nltk/nltk/wiki/Installing-Third-Party-Software

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

Movies Recommendation:

MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html

	// Requires the gpt library from https://github.com/hrishioa/socrate and the progress bar library.
	// Created by Hrishi Olickel (hrishioa@gmail.com) (@hrishioa). Reach out if you have trouble running this.

	import { ThunkQueue } from '../../utils/simplethrottler';
	import {
	AcceptedModels,
	Messages,
	askChatGPT,
	getMessagesTokenCount,
	getProperJSONFromGPT,

	#!/bin/bash

	### steps ####
	# Verify the system has a cuda-capable gpu
	# Download and install the nvidia cuda toolkit and cudnn
	# Setup environmental variables
	# Verify the installation
	###

	### to verify your gpu is cuda enable check

	nodes = [{'name': n, 'group': G.node[n]['question_id'], 'size': G.node[n]['count']} for n in G]
	l = G.edges()
	edges = [{'source': l.index(s), 'target': l.index(t), 'value': G[s][t]['weight']} for s,t in itertools.product(l, l) if s in G and t in G[s]]
	json.dump({'nodes': nodes, 'links': edges}, open('filename.json', 'w'))