sstadelman

Used Google Cloud Platform BigQuery WebUI to edit SQL queries for GOOGL prediction code competition.

Found some very good examples of BigQuery complex queries here. Really critical findings are:

1. Can use lag(<prop name>, <previous row count) over (ORDER BY <prop name>), and lead to access the window of the result set.
2. When combined with functions like AVG(<prop name>), makes it really easy to calculate moving averages

 SELECT
   DATE(S1.date) AS join_date,

sstadelman

I watched a 9 minute video on Long Short Term Memory (LSTM) in recurrent neural networks. The concept here is that the input to the hidden layer includes some information about the previous back-prop, which if I remember correctly includes the last calculation of the inner weights for each of the classes... something like that.

I walked through the jupyter notebook for this example. It introduces keras as an API, as well as some concepts around Dropout which I don't yet understand.

The very interesting thing about this example was the charting at the end, which plotted a set of predictions, against the true values. Also, the author proposed a code challenge, to best predict the future of GOOGL (Alphabet), using Price History and two other metrics using an LSTM.

I downloaded a dataset from Quandl for GOGVIX, curled to csv, and uploaded to BigQuery.

curl https://w

sstadelman

Ran Google Cloud Platform Big Query examples for working with time-series financial data. Used gcloud command-line tools to connect to Google Cloud project financials-test. Initialized bq (BigQuery tools), and pointed it at my project. Imported two large csv files of USD-GBP exchange rates in 2014.

# my GCP project
gcloud config set project financials-test

# my temporary data set that is imported to BigQuery
bq mk timeseries

# note the column definitions appended to the `load` command

sstadelman

Installing R

• Install the latest package of XQuartz from here: https://www.xquartz.org/releases/index.html
• Download and install the latest R from here: https://cran.rstudio.com

Load CIFAR-10

From here

> system("curl https://raw.githubusercontent.com/mlampros/DataSets/master/cifar_10.zip -o cifar_10.zip")

sstadelman

open class DefaultContainer<Provider: DataServiceProvider>: DataService<Provider> {
    public override init(provider: Provider) {
        super.init(provider: provider)
        self.provider.metadata = DefaultContainerMetadata.document
    }

    // ...

    open func photos(query: DataQuery = DataQuery()) throws -> Array<Photo> {

sstadelman

export XSC_OPTIONS=
./build-all.sh
export XSC_OPTIONS=-pi
./build-mobile.sh swift

sstadelman

<?xml version="1.0" encoding="utf-8"?>
<edmx:Edmx xmlns:edmx="http://docs.oasis-open.org/odata/ns/edmx" Version="4.0">
<edmx:DataServices>
    <Schema xmlns="http://docs.oasis-open.org/odata/ns/edm" Namespace="Microsoft.OData.SampleService.Models.TripPin">
    <EnumType Name="PersonGender">
        <Member Name="Male" Value="0"/>
        <Member Name="Female" Value="1"/>
        <Member Name="Unknown" Value="2"/>
    </EnumType>
    <ComplexType Name="City">

sstadelman

iPhoneSE:     portrait    UIEdgeInsets(top: 0.0, left: 16.0, bottom: 0.0, right: 16.0)    // compact
iPhoneSE:     landscape   UIEdgeInsets(top: 0.0, left: 20.0, bottom: 0.0, right: 20.0)    // compact
iPhone6s:     portrait    UIEdgeInsets(top: 0.0, left: 16.0, bottom: 0.0, right: 16.0)    // compact
iPhone6s:     landscape   UIEdgeInsets(top: 0.0, left: 20.0, bottom: 0.0, right: 20.0)    // compact
iPhone6sPlus: portrait    UIEdgeInsets(top: 0.0, left: 20.0, bottom: 0.0, right: 20.0)    // compact
iPhone6sPlus: landscape   UIEdgeInsets(top: 0.0, left: 32.0, bottom: 0.0, right: 32.0)    // regular
iPhone7:      portrait    UIEdgeInsets(top: 0.0, left: 16.0, bottom: 0.0, right: 16.0)    // compact 
iPhone7:      landscape   UIEdgeInsets(top: 0.0, left: 20.0, bottom: 0.0, right: 20.0)    // compact
iPhone7Plus:  portrait    UIEdgeInsets(top: 0.0, left: 20.0, bottom: 0.0, right: 20.0)    // compact
iPhone7Plus:  landscape   UIEdgeInsets(top: 0.0, left: 32.0, bottom: 0.0, right: 32.0)    // regular

sstadelman

# Switch to older version (e.g.:  v0.10.39)
sudo n 0.10.39
  #   install : node-v0.10.39
  #     mkdir : /usr/local/n/versions/node/0.10.39
  #     fetch : https://nodejs.org/dist/v0.10.39/node-v0.10.39-darwin-x64.tar.gz
  # installed : v0.10.39
  
# Switch to current version
sudo n stable
  #   install : node-v0.12.5

sstadelman

{
  "links" : [
    {"source": "5530bcc5298d000e396ff280", "target": "5530bd47298d000e396ff281"},
    {"source": "5530bd47298d000e396ff281", "target": "5530bd83298d000e396ff282"},
    {"source": "5530bd47298d000e396ff281", "target": "5530bdcb298d000e396ff283"},
    {"source": "5530bd83298d000e396ff282", "target": "5530be68298d000e396ff285"},
    {"source": "5530bdcb298d000e396ff283", "target": "5530bd83298d000e396ff282"},
    {"source": "5530bdcb298d000e396ff283", "target": "5530be28298d000e396ff284"},
    {"source": "5530be28298d000e396ff284", "target": "5530bd83298d000e396ff282"},
    {"source": "5530be28298d000e396ff284", "target": "5530be93298d000e396ff286"}