- Method to visualize high-dimensional data points in 2/3 dimensional space.
- Data visualization techniques like Chernoff faces and graph approaches just provide a representation and not an interpretation.
- Dimensionality reduction techniques fail to retain both local and global structure of the data simultaneously. For example, PCA and MDS are linear techniques and fail on data lying on a non-linear manifold.
- t-SNE approach converts data into a matrix of pairwise similarities and visualizes this matrix.
- Based on SNE (Stochastic Neighbor Embedding)
- Link to paper
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import os | |
os.environ['OPENAI_API_KEY'] = '<Your OpenAI API Key>' | |
# See here on how to find your Zotero info: https://github.com/urschrei/pyzotero#quickstart | |
ZOTERO_USER_ID = '<Your Zotero User ID>' | |
ZOTERO_API_KEY = '<Your Zotero API Key>' | |
ZOTERO_COLLECTION_ID = '<Your Zotero Collection ID>' | |
question = 'What predictive models are used in materials discovery?' | |
# The following prompt instruction is injected to limit the number of keywords per query |
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var express = require('express'); | |
var mysql = require('mysql'); | |
var app = express(); | |
/// | |
/// Create connection to MySQL database server. | |
/// | |
function getMySQLConnection() { | |
return mysql.createConnection({ |
Overview: http://matplotlib.org/faq/usage_faq.html#usage
Multiple, manual Axes: https://python4mpia.github.io/plotting/advanced.html
Another intro article http://pbpython.com/effective-matplotlib.html
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
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# Compiled source # | |
################### | |
*.com | |
*.class | |
*.dll | |
*.exe | |
*.o | |
*.so | |
# Packages # |