Mining-the-Social-Web-3rd-Edition - The official online compendium for Mining the Social Web, 3rd Edition (O'Reilly, 2018)

Overview

Mining the Social Web, 3rd Edition

The official code repository for Mining the Social Web, 3rd Edition (O'Reilly, 2019). The book is available from Amazon and Safari Books Online.

The notebooks folder of this repository contains the latest bug-fixed sample code used in the book chapters.

Quickstart

Binder

The easiest way to start playing with code right away is to use Binder. Binder is a service that takes a GitHub repository containing Jupyter Notebooks and spins up a cloud-based server to run them. You can start experimenting with the code without having to install anything on your machine. Click the badge above, or follow this link to get started right away.

NOTE: Binder will not save your files on its servers. During your next session, it will be a completely fresh instantiation of this repository. If you need a more persistent solution, consider running the code on your own machine.

Getting started on your own machine using Docker

  1. Install Docker
  2. Install repo2docker: pip install jupyter-repo2docker
  3. From the command line:
repo2docker https://github.com/mikhailklassen/Mining-the-Social-Web-3rd-Edition

This will create a Docker container from the repository directly. It takes a while to finish building the container, but once it's done, you will see a URL printed to screen. Copy and paste the URL into your browser.

A longer set of instructions can be found here.

Getting started on your own machine from source

If you are familiar with git and have a git client installed on your machine, simply clone the repository to your own machine. However, it is up to you to install all the dependencies for the repository. The necessary Python libraries are detailed in the requirements.txt file. The other requirements are detailed in the Requirements section below.

If you prefer not to use a git client, you can instead download a zip archive directly from GitHub. The only disadvantage of this approach is that in order to synchronize your copy of the code with any future bug fixes, you will need to download the entire repository again. You are still responsible for installing any dependencies yourself.

Install all the prerequisites using pip:

pip install -r requirements.txt

Once you're done, step into the notebooks directory and launch the Jupyter notebook server:

jupyter notebook

Side note on MongoDB

If you wish to complete all the examples in Chapter 9, you will need to install MongoDB. We do not provide support on how to do this. This is for more advanced users and is really only relevant to a few examples in Chapter 9.

Contributing

There are several ways in which you can contribute to the project. If you discover a bug in any of the code, the first thing to do is to create a new issue under the Issues tab of this repository. If you are a developer and would like to contribute a bug fix, please feel free to fork the repository and submit a pull request.

The code is provided "as-is" and we make no guarantees that it is bug-free. Keep in mind that we access the APIs of various social media platforms and their APIs are subject to change. Since the start of this project, various social media platforms have tightened the permissions on their platform. Getting full use out of all the code in this book may require submitting an application the social media platform of your choice for approval. Despite these restrictions, we hope that the code still provides plenty of flexibility and opportunities to go deeper.

Owner
Mikhail Klassen
Co-Founder and CTO at @PaladinAI. PhD, astrophysics. I specialize in machine learning, AI, data mining, and data visualization.
Mikhail Klassen
Re-TACRED: Addressing Shortcomings of the TACRED Dataset

Re-TACRED Re-TACRED: Addressing Shortcomings of the TACRED Dataset

George Stoica 40 Dec 10, 2022
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

The first comprehensive Robustness investigation benchmark on large-scale dataset ImageNet regarding ARchitecture design and Training techniques towards diverse noises.

132 Dec 23, 2022
BiSeNet based on pytorch

BiSeNet BiSeNet based on pytorch 0.4.1 and python 3.6 Dataset Download CamVid dataset from Google Drive or Baidu Yun(6xw4). Pretrained model Download

367 Dec 26, 2022
Model search is a framework that implements AutoML algorithms for model architecture search at scale

Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers speed up their exploration process for finding the right model a

Google 3.2k Dec 31, 2022
Barlow Twins and HSIC

Barlow Twins and HSIC Unofficial Pytorch implementation for Barlow Twins and HSIC_SSL on small datasets (CIFAR10, STL10, and Tiny ImageNet). Correspon

Yao-Hung Hubert Tsai 49 Nov 24, 2022
Image inpainting using Gaussian Mixture Models

dmfa_inpainting Source code for: MisConv: Convolutional Neural Networks for Missing Data (to be published at WACV 2022) Estimating conditional density

Marcin Przewięźlikowski 8 Oct 09, 2022
A medical imaging framework for Pytorch

Welcome to MedicalTorch MedicalTorch is an open-source framework for PyTorch, implementing an extensive set of loaders, pre-processors and datasets fo

Christian S. Perone 799 Jan 03, 2023
noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.

ProSelfLC: CVPR 2021 ProSelfLC: Progressive Self Label Correction for Training Robust Deep Neural Networks For any specific discussion or potential fu

amos_xwang 57 Dec 04, 2022
Transformer - Transformer in PyTorch

Transformer 完成进度 Embeddings and PositionalEncoding with example. MultiHeadAttent

Tianyang Li 1 Jan 06, 2022
Distributed Asynchronous Hyperparameter Optimization better than HyperOpt.

UltraOpt : Distributed Asynchronous Hyperparameter Optimization better than HyperOpt. UltraOpt is a simple and efficient library to minimize expensive

98 Aug 16, 2022
An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi

MetaICL: Learning to Learn In Context This includes an original implementation of "MetaICL: Learning to Learn In Context" by Sewon Min, Mike Lewis, Lu

Meta Research 141 Jan 07, 2023
Implementation of fast algorithms for Maximum Spanning Tree (MST) parsing that includes fast ArcMax+Reweighting+Tarjan algorithm for single-root dependency parsing.

Fast MST Algorithm Implementation of fast algorithms for (Maximum Spanning Tree) MST parsing that includes fast ArcMax+Reweighting+Tarjan algorithm fo

Miloš Stanojević 11 Oct 14, 2022
A pure PyTorch implementation of the loss described in "Online Segment to Segment Neural Transduction"

ssnt-loss ℹ️ This is a WIP project. the implementation is still being tested. A pure PyTorch implementation of the loss described in "Online Segment t

張致強 1 Feb 09, 2022
[UNMAINTAINED] Automated machine learning for analytics & production

auto_ml Automated machine learning for production and analytics Installation pip install auto_ml Getting started from auto_ml import Predictor from au

Preston Parry 1.6k Jan 02, 2023
PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Efficient Neural Architecture Search (ENAS) in PyTorch PyTorch implementation of Efficient Neural Architecture Search via Parameters Sharing. ENAS red

Taehoon Kim 2.6k Dec 31, 2022
Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning

Machine_Learning Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning This project is based on 2 case-studies:

Avnika Mehta 1 Jan 27, 2022
An open source implementation of CLIP.

OpenCLIP Welcome to an open source implementation of OpenAI's CLIP (Contrastive Language-Image Pre-training). The goal of this repository is to enable

2.7k Dec 31, 2022
Domain Generalization with MixStyle, ICLR'21.

MixStyle This repo contains the code of our ICLR'21 paper, "Domain Generalization with MixStyle". The OpenReview link is https://openreview.net/forum?

Kaiyang 208 Dec 28, 2022
neural image generation

pixray Pixray is an image generation system. It combines previous ideas including: Perception Engines which uses image augmentation and iteratively op

dribnet 398 Dec 17, 2022
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

LexGLUE: A Benchmark Dataset for Legal Language Understanding in English ⚖️ 🏆 🧑‍🎓 👩‍⚖️ Dataset Summary Inspired by the recent widespread use of th

95 Dec 08, 2022