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
Chainer Implementation of Semantic Segmentation using Adversarial Networks

Semantic Segmentation using Adversarial Networks Requirements Chainer (1.23.0) Differences Use of FCN-VGG16 instead of Dilated8 as Segmentor. Caution

Taiki Oyama 99 Jun 28, 2022
Hierarchical Uniform Manifold Approximation and Projection

HUMAP Hierarchical Manifold Approximation and Projection (HUMAP) is a technique based on UMAP for hierarchical non-linear dimensionality reduction. HU

Wilson Estécio Marcílio Júnior 160 Jan 06, 2023
FaRL for Facial Representation Learning

FaRL for Facial Representation Learning This repo hosts official implementation of our paper General Facial Representation Learning in a Visual-Lingui

Microsoft 19 Jan 05, 2022
The source code for CATSETMAT: Cross Attention for Set Matching in Bipartite Hypergraphs

catsetmat The source code for CATSETMAT: Cross Attention for Set Matching in Bipartite Hypergraphs To be able to run it, add catsetmat to PYTHONPATH H

2 Dec 19, 2022
Stream images from a connected camera over MQTT, view using Streamlit, record to file and sqlite

mqtt-camera-streamer Summary: Publish frames from a connected camera or MJPEG/RTSP stream to an MQTT topic, and view the feed in a browser on another

Robin Cole 183 Dec 16, 2022
Exploring Relational Context for Multi-Task Dense Prediction [ICCV 2021]

Adaptive Task-Relational Context (ATRC) This repository provides source code for the ICCV 2021 paper Exploring Relational Context for Multi-Task Dense

David Brüggemann 35 Dec 05, 2022
A Tensorfflow implementation of Attend, Infer, Repeat

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models This is an unofficial Tensorflow implementation of Attend, Infear, Repeat (AIR)

Adam Kosiorek 82 May 27, 2022
PyTorch implementation of SQN based on CloserLook3D's encoder

SQN_pytorch This repo is an implementation of Semantic Query Network (SQN) using CloserLook3D's encoder in Pytorch. For TensorFlow implementation, che

PointCloudYC 1 Oct 21, 2021
This repo tries to recognize faces in the dataset you created

YÜZ TANIMA SİSTEMİ Bu repo oluşturacağınız yüz verisetlerini tanımaya çalışan ma

Mehdi KOŞACA 2 Dec 30, 2021
Image-popularity-score - A novel deep regression method for image scoring.

Image-popularity-score - A novel deep regression method for image scoring.

Shoaib ahmed 1 Dec 26, 2021
The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark."

FFA-IR The official start-up code for paper "FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark." The framework is inheri

Mingjie 28 Dec 16, 2022
Manipulation OpenAI Gym environments to simulate robots at the STARS lab

Manipulator Learning This repository contains a set of manipulation environments that are compatible with OpenAI Gym and simulated in pybullet. In par

STARS Laboratory 5 Dec 08, 2022
KE-Dialogue: Injecting knowledge graph into a fully end-to-end dialogue system.

Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems This is the implementation of the paper: Learning Knowledge Bases with Par

CAiRE 42 Nov 10, 2022
Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set

Explaining Deep Neural Networks - A comparison of different CAM methods based on an insect data set This is the repository for the Deep Learning proje

Robert Krug 3 Feb 06, 2022
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding

The Hypersim Dataset For many fundamental scene understanding tasks, it is difficult or impossible to obtain per-pixel ground truth labels from real i

Apple 1.3k Jan 04, 2023
Modification of convolutional neural net "UNET" for image segmentation in Keras framework

ZF_UNET_224 Pretrained Model Modification of convolutional neural net "UNET" for image segmentation in Keras framework Requirements Python 3.*, Keras

209 Nov 02, 2022
This is a re-implementation of TransGAN: Two Pure Transformers Can Make One Strong GAN (CVPR 2021) in PyTorch.

TransGAN: Two Transformers Can Make One Strong GAN [YouTube Video] Paper Authors: Yifan Jiang, Shiyu Chang, Zhangyang Wang CVPR 2021 This is re-implem

Ahmet Sarigun 79 Jan 05, 2023
A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

StyleGAN3 CLIP-based guidance StyleGAN3 + CLIP StyleGAN3 + inversion + CLIP This repo is a collection of Jupyter notebooks made to easily play with St

Eugenio Herrera 176 Dec 30, 2022
Tutorial on scikit-learn and IPython for parallel machine learning

Parallel Machine Learning with scikit-learn and IPython Video recording of this tutorial given at PyCon in 2013. The tutorial material has been rearra

Olivier Grisel 1.6k Dec 26, 2022
Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer)

Computational modelling of ray propagation through optical elements using the principles of geometric optics (Ray Tracer) Introduction By applying the

Son Gyo Jung 1 Jul 09, 2022