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
Gapmm2: gapped alignment using minimap2 (align transcripts to genome)

gapmm2: gapped alignment using minimap2 This tool is a wrapper for minimap2 to r

Jon Palmer 2 Jan 27, 2022
Open & Efficient for Framework for Aspect-based Sentiment Analysis

PyABSA - Open & Efficient for Framework for Aspect-based Sentiment Analysis Fast & Low Memory requirement & Enhanced implementation of Local Context F

YangHeng 567 Jan 07, 2023
2021-AIAC-QQ-Browser-Hyperparameter-Optimization-Rank6

2021-AIAC-QQ-Browser-Hyperparameter-Optimization-Rank6

Aigege 8 Mar 31, 2022
Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm

Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetu

3 Dec 05, 2022
QueryFuzz implements a metamorphic testing approach to test Datalog engines.

Datalog is a popular query language with applications in several domains. Like any complex piece of software, Datalog engines may contain bugs. The mo

34 Sep 10, 2022
3D Avatar Lip Syncronization from speech (JALI based face-rigging)

visemenet-inference Inference Demo of "VisemeNet-tensorflow" VisemeNet is an audio-driven animator centric speech animation driving a JALI or standard

Junhwan Jang 17 Dec 20, 2022
It is an open dataset for object detection in remote sensing images.

RSOD-Dataset It is an open dataset for object detection in remote sensing images. The dataset includes aircraft, oiltank, playground and overpass. The

136 Dec 08, 2022
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN

Overview PyTorch 0.4.1 | Python 3.6.5 Annotated implementations with comparative introductions for minimax, non-saturating, wasserstein, wasserstein g

Shayne O'Brien 471 Dec 16, 2022
PyTorch implementation of the Deep SLDA method from our CVPRW-2020 paper "Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis"

Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis This is a PyTorch implementation of the Deep Streaming Linear Discriminant

Tyler Hayes 41 Dec 25, 2022
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

Keon Lee 140 Dec 21, 2022
Covid-19 Test AI (Deep Learning - NNs) Software. Accuracy is the %96.5, loss is the 0.09 :)

Covid-19 Test AI (Deep Learning - NNs) Software I developed a segmentation algorithm to understand whether Covid-19 Test Photos are positive or negati

Emirhan BULUT 28 Dec 04, 2021
PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech

PortaSpeech - PyTorch Implementation PyTorch Implementation of PortaSpeech: Portable and High-Quality Generative Text-to-Speech. Model Size Module Nor

Keon Lee 279 Jan 04, 2023
Accelerate Neural Net Training by Progressively Freezing Layers

FreezeOut A simple technique to accelerate neural net training by progressively freezing layers. This repository contains code for the extended abstra

Andy Brock 203 Jun 19, 2022
[CVPR'21] MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation

MonoRUn MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. CVPR 2021. [paper] Hansheng Chen, Yuyao Huang, Wei Tian*

同济大学智能汽车研究所综合感知研究组 ( Comprehensive Perception Research Group under Institute of Intelligent Vehicles, School of Automotive Studies, Tongji University) 96 Dec 10, 2022
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (ECCV 2020 Oral and TRO)

Visual Interestingness Refer to the project description for more details. This code based on the following paper. Chen Wang, Yuheng Qiu, Wenshan Wang,

Chen Wang 36 Sep 08, 2022
naked is a Python tool which allows you to strip a model and only keep what matters for making predictions.

naked is a Python tool which allows you to strip a model and only keep what matters for making predictions. The result is a pure Python function with no third-party dependencies that you can simply c

Max Halford 24 Dec 20, 2022
Image data augmentation scheduler for albumentations transforms

albu_scheduler Scheduler for albumentations transforms based on PyTorch schedulers interface Usage TransformMultiStepScheduler import albumentations a

19 Aug 04, 2021
AI-Bot - 一个基于watermelon改造的OpenAI-GPT-2的智能机器人

AI-Bot 一个基于watermelon改造的OpenAI-GPT-2的智能机器人 在Binder上直接运行测试 目前有两种实现方式 TF2的GPT-2 TF

9 Nov 16, 2022
Paddle-Skeleton-Based-Action-Recognition - DecoupleGCN-DropGraph, ASGCN, AGCN, STGCN

Paddle-Skeleton-Action-Recognition DecoupleGCN-DropGraph, ASGCN, AGCN, STGCN. Yo

Chenxu Peng 3 Nov 02, 2022
Linescanning - Package for (pre)processing of anatomical and (linescanning) fMRI data

line scanning repository This repository contains all of the tools used during the acquisition and postprocessing of line scanning data at the Spinoza

Jurjen Heij 4 Sep 14, 2022