Exercises_5_16_2017.md

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://www.quandl.com/api/v3/datasets/CBOE/VXGOG.csv?api_key=A7vbfQ4yMyk3uzUyhC8s -o CBOE_VIXGOG.csv
curl https://www.quandl.com/api/v3/datasets/WIKI/GOOGL.csv?api_key=A7vbfQ4yMyk3uzUyhC8s -o WIKI_GOOGL.csv

And, a quick test to validate the data:

SELECT
  TIMESTAMP(date) as date,
  high
FROM
  [quandl_googl_vix.vixgog]
ORDER BY
  date ASC
LIMIT
  1000;

Row	date	high
1	2010-06-01 00:00:00 UTC	41.0
2	2010-06-02 00:00:00 UTC	40.85
3	2010-06-03 00:00:00 UTC	37.78
4	2010-06-04 00:00:00 UTC	39.76
5	2010-06-07 00:00:00 UTC	41.56

Next Steps

1. Also upload the Quandl data for the #change calculation (or calculate locally)
2. Upload the source GOOGL OHLC data

Future resources

• Forecasting with the Long Short-Term Memory Network in Python Apr 7, 2017
• Using Google BigQuery With Plotly and Pandas Jul 23, 2016