Monday, January 14, 2019

Changing the markers for Google Maps and centering map

In some recent work, I've been integrating with Google Maps and some of the other Google API's a lot. This post is just a reminder for myself on how to change the format, colour, and other properties of the map pointers.

cluster_0_gmap = gmaps.symbol_layer(
    map_locations_c0, fill_color='red',
    stroke_color='red', scale=5 )

cluster_1_gmap = gmaps.symbol_layer(
    map_locations_c1, fill_color='green',
    stroke_color='green', scale=5 )

cluster_2_gmap = gmaps.symbol_layer(
    map_locations_c2, fill_color='purple',
    stroke_color='purple', scale=5 )

cluster_3_gmap = gmaps.symbol_layer(
    map_locations_c3, fill_color='blue',
    stroke_color='blue', scale=5 )

And now for the map initial settings, centred on Athlone town in the middle of Ireland.

fig = gmaps.figure()

figure_layout = {
'width': '950px',
'height': '730px',
'border': '1px solid black',
'padding': '1px',
'margin': '0 auto 0 auto'
}

ireland_coord = (53.42, -7.94)
fig=gmaps.figure(center=ireland_coord, zoom_level=7.5, layout=figure_layout)

fig.add_layer(cluster_0_gmap)
fig.add_layer(cluster_1_gmap)
fig.add_layer(cluster_2_gmap)
fig.add_layer(cluster_3_gmap)
fig

Friday, January 4, 2019

Understanding, Building and Using Neural Network Models using Oracle 18c

I recently had an article published on Oracle Developer Community website about Understanding, Building and Using Neural Network Machine Learning Models with Oracle 18c. I've also had a 2 Minute Tech Tip (2MTT) video about this topic and article.

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Oracle 18c Database brings prominent new machine learning algorithms, including Neural Networks and Random Forests. While many articles are available on machine learning, most of them concentrate on how to build a model. Very few talk about how to use these new algorithms in your applications to score or label new data. This article will explain how Neural Networks work, how to build a Neural Network in Oracle Database, and how to use the model to score or label new data. What are Neural Networks?

Over the past couple of years, Neural Networks have attracted a lot of attention thanks to their ability to efficiently find patterns in data—traditional transactional data, as well as images, sound, streaming data, etc. But for some implementations, Neural Networks can require a lot of additional computing resources due to the complexity of the many hidden layers within the network. Figure 1 gives a very simple representation of a Neural Network with one hidden layer. All the inputs are connected to a neuron in the hidden layer (red circles). A neuron takes a set of numeric values as input and maps them to a single output value. (A neuron is a simple multi-input linear regression function, where the output is passed through an activation function.) Two common activation functions are logistic and tanh functions. There are many others, including logistic sigmoid function, arctan function, bipolar sigmoid function, etc.

Continue reading the rest of the article here.

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