octocat

# Compiled source #
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*.com
*.class
*.dll
*.exe
*.o
*.so

# Packages #

karpathy

"""
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)

ax3l

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

Great Foo

shagunsodhani

Visualizing Data using t-SNE

Introduction

• 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

rakawestu

var express = require('express');
var mysql = require('mysql');

var app = express();

///
///	Create connection to MySQL database server.
/// 
function getMySQLConnection() {
	return mysql.createConnection({

lifan0127

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