awmatheson

dkvasnicka

FROM gcr.io/deepnote-200602/templates/deepnote

RUN sudo apt-get update && \
    sudo apt-get install -y libzmq5
    
# Derived from https://github.com/jackfirth/racket-docker which is
# licensed under the MIT license.

ENV RACKET_INSTALLER_URL=http://mirror.racket-lang.org/installers/7.8/racket-7.8-x86_64-linux-natipkg.sh 
ENV RACKET_VERSION=7.8

omarsar

Title

Applied Deep Learning for NLP Applications

Abstract

Natural language processing (NLP) has become an important field with interest from many important sectors that leverage modern deep learning methods for approaching several NLP problems and tasks such as text summarization, question answering, and sentiment classification, to name a few. In this tutorial, we will introduce several of the fundamental NLP techniques and more modern approaches (BERT, GTP-2, etc.) and show how they can be applied via transfer learning to approach many real-world NLP problems. We will focus on how to build an NLP pipeline using several open-source tools such as Transformers, Tokenizers, spaCy, TensorFlow, and PyTorch, among others. Then we will learn how to use the NLP model to search over documents based on semantic relationships. We will use open-source technologies such as BERT and Elasticsearch for this segment to build a proof of concept. In essence, the learner will take away the important theoretical pieces ne

devin-petersohn

karlafej

gvanrossum

Hello Dear,

This is Ellen from ***.com

We are pleased to get to know that your articles on your blog are awesome and the writing style is so suitable for introducing our company's products.

And we sincerely invite you to cooperate with us,

*Write an article about our products on your blog about 400-500 words + 2 pictures+ 3 links  
*Promote it on your social website.

thomasp85

library(tidygraph)
library(particles)
library(jsonlite)
library(magick)

# Prepare text polygons
text <- read_json('text.json')
par_text <- text$layers[[3]]$paths
on_text <- text$layers[[2]]$paths
cran_text <- text$layers[[1]]$paths

stephlocke

• Have a Code of Conduct and a demonstrable commitment to diversity
  • The Code of Conduct must be prominently displayed
  • Sponsors and other third parties must adhere to the Code of Conduct
• Provide a mechanism for low income attendees to get reduced prices and support where the event charges more than a day's income
• Attendees encounter only non-defaulted opt-ins to marketing and future contact, especially from third parties
• The data entrusted to the event is handled with due care and consideration
• Diverse attendees' needs are considered and taken into consideration. Things like (but not limited to) large print agendas, gender neutral bathrooms, quiet rooms, family rooms, and prayer rooms are implemented to ensure a pleasant experience for all attendee
• Volunteers and organisers should receive reduced or free entry to the event
• New speakers are encouraged and offered extra support
• The speaker selection process is performed in a way that reduces possible sources of bias

ikosmidis

## Licence: GPL 2 or 3 <https://www.gnu.org/licenses/licenses.html#GPL>
## Author: Ioannis Kosmidis <i.kosmidis@ucl.ac.uk>
## Date: 21 December 2017

#' A function to create artifical snow using R base graphics and the animation package
#'
#' @param n_flakes how many flakes to throw?
#' @param fall_speed how fast should the snow fall? There will be a \code{1/fall_speed} seconds delay between moves
#' @param max_flake_size what is the maximum flake size (same as cex in \code{\link{plot}})
#' @param eps how much is the flake allowed to move to the left and to the right? (emulating wind)

famanson

"WTF is... Machine Learning" coding session setup

1. Download Anaconda distribution Python version 3.6 from here.

2. Follow the installation instructions for your system here.

3. Download the necessary files using this link

4. Move the zip file to your Documents folder and unzip it.