llSourcell

#Step 1 - clone universe 
#git clone https://github.com/openai/universe.git
#cd universe
#pip install -e . (Editable mode)   It makes installed packages editable. 
#And its reading which packages to download from setup.py

#Step 2 - install command line tools

#Step 3 - Install 3 more packages. Homebrew is like apt-get for OS X.

llSourcell

llSourcell

#easier
https://github.com/bhaktipriya/Blues

##Explain what he did, why we need better music 
modules in python
#1 first tried a simple RNN with 2 LSTM layers
#2 88 binary classification problem
#3 multiclass classification, (two at same time)
sigmoid cross entropy instead of softmax
#4 Music is complex. 

llSourcell

//Define 10x20 grid as the board
var grid = [
[0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0],

llSourcell

import numpy as np

# input data
X = np.array([  [0,0,1],
                [0,1,1],
                [1,0,1],
                [1,1,1] ])
    
# output labels            
y = np.array([[0,0,1,1]]).T

llSourcell

 while norm > epsilon:
        iteration += 1
        norm = dist_method(prototypes, prototypes_old)
        prototypes_old = prototypes
        #for each instance in the dataset
        for index_instance, instance in enumerate(dataset):
            #define a distance vector of size k
            dist_vec = np.zeros((k, 1))
            #for each centroid
            for index_prototype, prototype in enumerate(prototypes):

llSourcell

        #first we feed the input image into a convolutional layer to get a feature map
		h = self.cnn_layer(X, layer_i=0, border_mode="full") ; X = h
        #then we acivate it with a nonlinearity to make sure the math doesn't break (turn all neg numbers to 0)
		h = self.relu_layer(X) ; X = h
        #another CNN layer for more, smaller images (more hierarchical features = better prediction)
		h = self.cnn_layer(X, layer_i=2, border_mode="valid") ; X = h
        #another nonlinearity 
		h = self.relu_layer(X) ; X = h
        #pooling to reduce computational cost, extract the most important features
		h = self.maxpooling_layer(X) ; X = h

llSourcell

pragma solidity ^0.4.4;

contract Token {

    /// @return total amount of tokens
    function totalSupply() constant returns (uint256 supply) {}

    /// @param _owner The address from which the balance will be retrieved
    /// @return The balance
    function balanceOf(address _owner) constant returns (uint256 balance) {}

llSourcell

Number Target
1      0
2      1
3      0
4      1
5      0
6      1
...   ...
99     0
100    1

llSourcell

import tweepy
import praw
import coindesk
import dict_list
import pandas as pd
import word2vec as w2v

#Step 1 - Retrieve Tweets past 30 days
twitter_config = json.load(TWEEPY_CONFIG_FILE)