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
PyTorch - Python + Nim

Master Release Pytorch - Py + Nim A Nim frontend for pytorch, aiming to be mostly auto-generated and internally using ATen. Because Nim compiles to C+

Giovanni Petrantoni 425 Dec 22, 2022
A super lightweight Lagrangian model for calculating millions of trajectories using ERA5 data

Easy-ERA5-Trck Easy-ERA5-Trck Galleries Install Usage Repository Structure Module Files Version iteration Easy-ERA5-Trck is a super lightweight Lagran

Zhenning Li 26 Nov 19, 2022
Face Recognition plus identification simply and fast | Python

PyFaceDetection Face Recognition plus identification simply and fast Ubuntu Setup sudo pip3 install numpy sudo pip3 install cmake sudo pip3 install dl

Peyman Majidi Moein 16 Sep 22, 2022
Large scale embeddings on a single machine.

Marius Marius is a system under active development for training embeddings for large-scale graphs on a single machine. Training on large scale graphs

Marius 107 Jan 03, 2023
DualGAN-tensorflow: tensorflow implementation of DualGAN

ICCV paper of DualGAN DualGAN: unsupervised dual learning for image-to-image translation please cite the paper, if the codes has been used for your re

Jack Yi 252 Nov 10, 2022
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces Paper Temporary GitHub page for City Surfaces paper. More soon! While designing s

14 Nov 10, 2022
This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf

Behavior-Sequence-Transformer-Pytorch This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf This model

Jaime Ferrando Huertas 83 Jan 05, 2023
Code for paper: Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks

Group-CAM By Zhang, Qinglong and Rao, Lu and Yang, Yubin [State Key Laboratory for Novel Software Technology at Nanjing University] This repo is the o

zhql 98 Nov 16, 2022
Differentiable simulation for system identification and visuomotor control

gradsim gradSim: Differentiable simulation for system identification and visuomotor control gradSim is a unified differentiable rendering and multiphy

105 Dec 18, 2022
Bootstrapped Unsupervised Sentence Representation Learning (ACL 2021)

Install first pip3 install -e . Training python3 training/unsupervised_tuning.py python3 training/supervised_tuning.py python3 training/multilingual_

yanzhang_nlp 26 Jul 22, 2022
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course

Deep Learning with TensorFlow 2 and Keras – Notebooks This project accompanies my Deep Learning with TensorFlow 2 and Keras trainings. It contains the

Aurélien Geron 1.9k Dec 15, 2022
Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt)

Deep Learning for Natural Language Processing SS 2021 (TU Darmstadt) Task Training huge unsupervised deep neural networks yields to strong progress in

2 Aug 05, 2022
Saeed Lotfi 28 Dec 12, 2022
Dynamic Realtime Animation Control

Our project is targeted at making an application that dynamically detects the user’s expressions and gestures and projects it onto an animation software which then renders a 2D/3D animation realtime

Harsh Avinash 10 Aug 01, 2022
This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

This is the codebase for Diffusion Models Beat GANS on Image Synthesis.

OpenAI 3k Dec 26, 2022
Code of the paper "Shaping Visual Representations with Attributes for Few-Shot Learning (ASL)".

Shaping Visual Representations with Attributes for Few-Shot Learning This code implements the Shaping Visual Representations with Attributes for Few-S

chx_nju 9 Sep 01, 2022
Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.

Neural Spatio-Temporal Point Processes [arxiv] Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Abstract. We propose a new class of parameterizations

Facebook Research 75 Dec 19, 2022
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)

Intro This repository contains code to generate data and reproduce experiments from our NeurIPS 2019 paper: Boris Knyazev, Graham W. Taylor, Mohamed R

Boris Knyazev 242 Jan 06, 2023
Multi-View Radar Semantic Segmentation

Multi-View Radar Semantic Segmentation Paper Multi-View Radar Semantic Segmentation, ICCV 2021. Arthur Ouaknine, Alasdair Newson, Patrick Pérez, Flore

valeo.ai 37 Oct 25, 2022
Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation

Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation Requirements This repository needs mmsegmentation Training To train

20 May 28, 2022