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time_series_prediction

This is the code for "Time Series Prediction" By Siraj Raval on Youtube

timebanks

What are the best predictors of an active timebank?

tinygbt

A Tiny, Pure Python implementation of Gradient Boosted Trees.

titan

Distributed Graph Database

titanic

Kaggle Titanic competition in TensorFlow

titanic-data-analysis

This notebook is a very basic and simple introductory primer to the method of ensembling models, in particular the variant of ensembling known as Stacking. In a nutshell stacking uses as a first-level (base), the predictions of a few basic machine learning models (classifiers) and then uses another model at the second-level to predict the output from the earlier first-level predictions. The Titanic dataset is a prime candidate for introducing this concept as many newcomers to Kaggle start out here. Furthermore even though stacking has been responsible for many a team winning Kaggle competitions there seems to be a dearth of kernels on this topic so I hope this notebook can fill somewhat of that void. I myself am quite a newcomer to the Kaggle scene as well and the first proper ensembling/stacking script that I managed to chance upon and study was one written in the AllState Severity Claims competition by the great Faron. The material in this notebook borrows heavily from Faron's script although ported to factor in ensembles of classifiers whilst his was ensembles of regressors. Anyway please check out his script here: Stacking Starter : by Faron Now onto the notebook at hand and I hope that it manages to do justice and convey the concept of ensembling in an intuitive and concise manner. My other standalone Kaggle script which implements exactly the same ensembling steps (albeit with different parameters) discussed below gives a Public LB score of 0.808 which is good enough to get to the top 9% and runs just under 4 minutes. Therefore I am pretty sure there is a lot of room to improve and add on to that script. Anyways please feel free to leave me any comments with regards to how I can improve

titanic-kaggle

A solution of Kaggle's Titanic competition using a number of classifiers and a stacking approach.

titanic-stack

Experimenting with stacking using the Kaggle titanic data set / challenge.

toolbox

Curated list of libraries for a faster machine learning workflow

top2vec

Top2Vec learns jointly embedded topic, document and word vectors.

topic-modeling

topic-modeling-health-tweets

Exploration of Health-Related Tweets through Topic Modeling & Sentiment Analysis

topic-stability

Stability analysis for topic models

topic_modeling_online_reviews

topical_word_embeddings

A demo code for topical word embedding

toposketch

tpa-lstm

Temporal Pattern Attention for Multivariate Time Series Forecasting

tpot

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

tpot-pydatabcn

Examples of TPOT usage. PyData Barcelona 2017

tractable-neural-causal-model

Official code repository to the corresponding paper.

trajectory-transformer

Code for "Transformer Networks for Trajectory Forecasting"

transfer-learning-for-animal-classification-in-tensorflow

Siraj Raval's Weekly Challenge Accepted :sunglasses:

transfer_learning

transformer-xl

transformers

Ipython notebooks of walk-trough Transformer model implementations in PyTorch and GPT-2 fine-tuning.

transformers-interpret

Transformers Interpret is a model explainability tool designed to work exclusively with 🤗 transformers. It allows you to explain your model in just 2 lines of code.

transgan

[Preprint] "TransGAN: Two Transformers Can Make One Strong GAN", Yifan Jiang, Shiyu Chang, Zhangyang Wang

transmogrifai

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning

trapdoor

Material for Estimation of causal effects with small data under implicit functional constraints

tree-regularization-public

Code for AAAI 2018 accepted paper: "Beyond Sparsity: Tree Regularization of Deep Models for Interpretability"