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synode

Automatically Preventing Code Injection Attacks on Node.js

system-dashboard

Minimalist Win/OSX/Linux System Dashboard using Flask and Freeboard

system-design-primer

Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.

tadpole

Helper scripts for TADPOLE Challenge 2017: http://tadpole.grand-challenge.org

tarp

tcpkali

Fast multi-core TCP and WebSockets load generator.

tech_topic_meetup_blog

Repo with code and data for the analysis of new tech topics using Meetup data

telloeduswarmsearch

A Python library for interfacing with the Ryze Tello Edu, including swarm and search behaviours.

templacity

tensorflow-auto

Using TensorFlow Extended components to mix-and-match pipelines through evolutionary algorithms

tensorflow-course

Simple and ready-to-use tutorials for TensorFlow

terraform-training

AWS South Wales Meeting - Hands-on Workshop: Terraform on AWS

terrapattern

Enabling journalists, citizen scientists, humanitarian workers and others to detect “patterns of interest” in satellite imagery.

tesseract

Tesseract Open Source OCR Engine (main repository)

text-preprocessing-techniques

16 Text Preprocessing Techniques in Python for Twitter Sentiment Analysis.

text-scraping-document-clustering-topic-modeling

The objective of this project is to scrape a corpus of news articles from a set of web pages, pre-process the corpus, and then to apply unsupervised clustering algorithms to explore and summarise the contents of the corpus. Part 1. Text Data Scraping This part of the project should be implemented as a Python script 1. Identify the URLs for all news articles listed on the website: http://mlg.ucd.ie/modules/COMP41680/news/index.html 2. Retrieve all web pages corresponding to these article URLs. 3. From the web pages, extract the main body text containing the content of each news article. Save the body of each article as plain text. Part 2. Corpus Exploration Tasks to be completed in your IPython notebook: 1. Load the text corpus generated in Part 1. Apply any appropriate pre-processing steps and construct a document-term matrix representation of the corpus. 2. Summarise the overall corpus by identifying the most characteristic terms and phrases in the corpus. 3. Apply two alternative clustering algorithms of your choice to the document-term matrix to produce clusters of related documents. This might require applying each algorithm several times with different parameter values. 4. For each clustering generated in Step 3, summarise the contents of the clusters. Based on your summary, suggest a topic/theme for each cluster.

textvae

Theano code for experiments in the paper "A Hybrid Convolutional Variational Autoencoder for Text Generation."

theharvester

E-mails, subdomains and names Harvester - OSINT

themlbook

The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.

theyworkforyou

Keeping tabs on the UK's parliaments and assemblies

threat-hunting

Personal compilation of APT malware from whitepaper releases, documents and own research

threathunting

An informational repo about hunting for adversaries in your IT environment.

threatingestor

Extract and aggregate threat intelligence.

tidytuesday

Official repo for the #tidytuesday project

tika-img-similarity

tika-python

til

Things I learned project.

til-db

Things I Learned database.

timesketch

Collaborative forensic timeline analysis

tinfoleak

The most complete open-source tool for Twitter intelligence analysis