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
Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators

Pandas TA - A Technical Analysis Library in Python 3 Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package

Kevin Johnson 3.2k Jan 09, 2023
Multi Agent Path Finding Algorithms

MATP-solver Simulator collision check path step random initial states or given states Traditional method Seperate A* algorithem Confict-based Search S

30 Dec 12, 2022
Keeping it safe - AI Based COVID-19 Tracker using Deep Learning and facial recognition

Keeping it safe - AI Based COVID-19 Tracker using Deep Learning and facial recognition

Vansh Wassan 15 Jun 17, 2021
PyTorch Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

pytorch-fcn PyTorch implementation of Fully Convolutional Networks. Requirements pytorch = 0.2.0 torchvision = 0.1.8 fcn = 6.1.5 Pillow scipy tqdm

Kentaro Wada 1.6k Jan 07, 2023
DeepFaceLab fork which provides IPython Notebook to use DFL with Google Colab

DFL-Colab — DeepFaceLab fork for Google Colab This project provides you IPython Notebook to use DeepFaceLab with Google Colaboratory. You can create y

779 Jan 05, 2023
Segmentation models with pretrained backbones. PyTorch.

Python library with Neural Networks for Image Segmentation based on PyTorch. The main features of this library are: High level API (just two lines to

Pavel Yakubovskiy 6.6k Jan 06, 2023
Implementation supporting the ICCV 2017 paper "GANs for Biological Image Synthesis"

GANs for Biological Image Synthesis This codes implements the ICCV-2017 paper "GANs for Biological Image Synthesis". The paper and its supplementary m

Anton Osokin 95 Nov 25, 2022
Code reproduce for paper "Vehicle Re-identification with Viewpoint-aware Metric Learning"

VANET Code reproduce for paper "Vehicle Re-identification with Viewpoint-aware Metric Learning" Introduction This is the implementation of article VAN

EMDATA-AILAB 23 Dec 26, 2022
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

This is a curated list of tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch. Feel free to make a pu

Ritchie Ng 9.2k Jan 02, 2023
A denoising diffusion probabilistic model (DDPM) tailored for conditional generation of protein distograms

Denoising Diffusion Probabilistic Model for Proteins Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to gen

Phil Wang 108 Nov 23, 2022
MegEngine implementation of YOLOX

Introduction YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and ind

旷视天元 MegEngine 77 Nov 22, 2022
Simple and understandable swin-transformer OCR project

swin-transformer-ocr ocr with swin-transformer Overview Simple and understandable swin-transformer OCR project. The model in this repository heavily r

Ha YongWook 67 Dec 31, 2022
Code for Environment Inference for Invariant Learning (ICML 2020 UDL Workshop Paper)

Environment Inference for Invariant Learning This code accompanies the paper Environment Inference for Invariant Learning, which appears at ICML 2021.

Elliot Creager 40 Dec 09, 2022
Pytorch implementation of Bert and Pals: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

PyTorch implementation of BERT and PALs Introduction Work by Asa Cooper Stickland and Iain Murray, University of Edinburgh. Code for BERT and PALs; mo

Asa Cooper Stickland 70 Dec 29, 2022
Resources complimenting the Machine Learning Course led in the Faculty of mathematics and informatics part of Sofia University.

Machine Learning and Data Mining, Summer 2021-2022 How to learn data science and machine learning? Programming. Learn Python. Basic Statistics. Take a

Simeon Hristov 8 Oct 04, 2022
Code for "Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks", CVPR 2021

Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks This repository contains the code that accompanies our CVPR 20

Despoina Paschalidou 161 Dec 20, 2022
A micro-game "flappy bird".

1-o-flappy A micro-game "flappy bird". Gameplays The game will be installed at /usr/bin . The name of it is "1-o-flappy". You can type "1-o-flappy" to

1 Nov 06, 2021
Official PyTorch implementation of "The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person Pose Estimation" (ICCV 21).

CenterGroup This the official implementation of our ICCV 2021 paper The Center of Attention: Center-Keypoint Grouping via Attention for Multi-Person P

Dynamic Vision and Learning Group 43 Dec 25, 2022
Deep Learning Based Fasion Recommendation System for Ecommerce

Project Name: Fasion Recommendation System for Ecommerce A Deep learning based streamlit web app which can recommened you various types of fasion prod

BAPPY AHMED 13 Dec 13, 2022
A python program to hack instagram

hackinsta a program to hack instagram Yokoback_(instahack) is the file to open, you need libraries write on import. You run that file in the same fold

2 Jan 22, 2022