All Projects → kylemcdonald → Parametric T Sne

kylemcdonald / Parametric T Sne

Licence: mit
Running parametric t-SNE by Laurens Van Der Maaten with Octave and oct2py.

Projects that are alternatives of or similar to Parametric T Sne

Human body prior
VPoser: Variational Human Pose Prior
Stars: ✭ 244 (-0.81%)
Mutual labels:  jupyter-notebook
Conceptualsearch
Train a Word2Vec model or LSA model, and Implement Conceptual Search\Semantic Search in Solr\Lucene - Simon Hughes Dice.com, Dice Tech Jobs
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Audio Classification
Code for YouTube series: Deep Learning for Audio Classification
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Recmetrics
A library of metrics for evaluating recommender systems
Stars: ✭ 244 (-0.81%)
Mutual labels:  jupyter-notebook
Zhihu
知乎看山杯 第二名 解决方案
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Exploratory computing with python
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
2016 01 Tennis Betting Analysis
Methodology and code supporting the BuzzFeed News/BBC article, "The Tennis Racket," published Jan. 17, 2016.
Stars: ✭ 244 (-0.81%)
Mutual labels:  jupyter-notebook
Wav2mid
Automatic Music Transcription with Deep Neural Networks
Stars: ✭ 246 (+0%)
Mutual labels:  jupyter-notebook
Box Plots Sklearn
An implementation of some of the tools used by the winner of the box plots competition using scikit-learn.
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Bigquery Oreilly Book
Source code accompanying: BigQuery: The Definitive Guide by Lakshmanan & Tigani to be published by O'Reilly Media
Stars: ✭ 246 (+0%)
Mutual labels:  jupyter-notebook
Guided Evolutionary Strategies
Guided Evolutionary Strategies
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Link Prediction
Representation learning for link prediction within social networks
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Pomegranate
Fast, flexible and easy to use probabilistic modelling in Python.
Stars: ✭ 2,789 (+1033.74%)
Mutual labels:  jupyter-notebook
Delf Pytorch
PyTorch Implementation of "Large-Scale Image Retrieval with Attentive Deep Local Features"
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Scipy 2018 Sklearn
Scipy 2018 scikit-learn tutorial by Guillaume Lemaitre and Andreas Mueller
Stars: ✭ 247 (+0.41%)
Mutual labels:  jupyter-notebook
Fouriertalkoscon
Presentation Materials for my "Sound Analysis with the Fourier Transform and Python" OSCON Talk.
Stars: ✭ 244 (-0.81%)
Mutual labels:  jupyter-notebook
Jupyter Tips And Tricks
Using Project Jupyter for data science.
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Bayesian Optimization
Python code for bayesian optimization using Gaussian processes
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook
Mltrain Nips 2017
This repository contains all the material for the MLTrain NIPS workshop
Stars: ✭ 246 (+0%)
Mutual labels:  jupyter-notebook
Recsys core
[电影推荐系统] Based on the movie scoring data set, the movie recommendation system is built with FM and LR as the core(基于爬取的电影评分数据集,构建以FM和LR为核心的电影推荐系统).
Stars: ✭ 245 (-0.41%)
Mutual labels:  jupyter-notebook

Parametric t-SNE

Laurens Van Der Maaten's parametric implementation of t-SNE.

Laurens' original implementation is for Matlab, here we are running in Octave with oct2py in the notebook Parametric t-SNE (Original). This code can take hours to complete.

In the Parametric t-SNE (Keras) notebook there is an implementation of the same technique by reimplementing all functions in Python with numpy and Keras. The code runs significantly faster (on my machine, 20 minutes). There are also some work-in-progress experiments, like using the pairwise probabilty embedding to pre-train the weights of an autoencoder, which appears to converge to a lower reconstruction error than a vanilla autoencoder.

Setup

On OS X:

$ brew install octave
$ pip install -r requirements.txt
$ jupyter notebook
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].