Skip to content

Instantly share code, notes, and snippets.

View Diego999's full-sized avatar

Diego Antognini Diego999

View GitHub Profile
@aparrish
aparrish / spacy_intro.ipynb
Last active July 29, 2024 21:03
NLP Concepts with spaCy. Code examples released under CC0 https://creativecommons.org/choose/zero/, other text released under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
@jayktaylor
jayktaylor / guide.md
Last active April 13, 2022 08:23
Instructions for installing Python 3.5 using pyenv on Debian Jessie

Installing Python 3.5 on Debian Jessie with pyenv

Debian Jessie does not come with the correct Python version out of the box, and instead comes with Python 2. To be able to install Python 3(.5), we have a few options. We could build and install from source, but as per Debian's website, we shouldn't do this. Instead, we will use pyenv, a tool that allows users to switch Python versions easily without breaking their system.

Installing pyenv

To install pyenv, we will use the official installer.

curl -L https://raw.githubusercontent.com/yyuu/pyenv-installer/master/bin/pyenv-installer | bash
@bartvm
bartvm / dl-frameworks.rst
Last active December 7, 2020 18:18
A comparison of deep learning frameworks

A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.

Overview

Symbolic: Theano, CGT; Automatic: Torch, MXNet

Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.

@pratapvardhan
pratapvardhan / GFinSectorIndustryBasic.py
Created June 5, 2014 09:56
Python Script to extract Sector and Industry for a company stock listed on Google Finance
from urllib import urlopen
from lxml.html import parse
'''
Returns a tuple (Sector, Indistry)
Usage: GFinSectorIndustry('IBM')
'''
def GFinSectorIndustry(name):
tree = parse(urlopen('http://www.google.com/finance?&q='+name))
return tree.xpath("//a[@id='sector']")[0].text, tree.xpath("//a[@id='sector']")[0].getnext().text
@tsiege
tsiege / The Technical Interview Cheat Sheet.md
Last active November 14, 2024 15:40
This is my technical interview cheat sheet. Feel free to fork it or do whatever you want with it. PLEASE let me know if there are any errors or if anything crucial is missing. I will add more links soon.

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






\

@bwhite
bwhite / rank_metrics.py
Created September 15, 2012 03:23
Ranking Metrics
"""Information Retrieval metrics
Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np
@jboner
jboner / latency.txt
Last active November 18, 2024 08:23
Latency Numbers Every Programmer Should Know
Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference 0.5 ns
Branch mispredict 5 ns
L2 cache reference 7 ns 14x L1 cache
Mutex lock/unlock 25 ns
Main memory reference 100 ns 20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy 3,000 ns 3 us
Send 1K bytes over 1 Gbps network 10,000 ns 10 us
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD