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.

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.

Oracle Machine Learning notebooks

In this blog post I'll have a look at Oracle Machine Learning notebooks, some of the example notebooks and then how to create a new one.

Check out my previous blog posts on ADWC.

- Create an Autonomous Data Warehouse Cloud Service

- Creating and Managing OML user on ADWC

On entering Oracle Machine Learning on your ADWC service, you will get the following.

Our starting point is to example what is listed in the Examples section. Click on the Examples link. The following lists the example notebooks.

Here we have examples that demonstrate how to build Anomaly Detection, Association Rules, Attribute Importance, Classification, Regression, Clustering and one that contains examples of various statistical function.

Click on one of these to see the notebook. The following is the notebook demoing the Statistical Functions. When you select a notebook it might take a few seconds to setup and open. There is some setup needed in the background and to make sure you have access to the demo data and then runs the notebook, generating the results. Most of the demo data is based on the SH schema.

Now let us create our first notebook.

From the screen shown above lift on the menu icon on the top left of the screen.

And then click on Notebooks from the pop-out menu.

In the Notebooks screen click on the Create button to create your first notebook.

And give it a meaningful name.

The Notebook shell will be created and then opened for you.

In the grey box, just under the name the name of your Notebook, is where you can enter your first SQL statement. Then over on the right hand side of this Cell you will see a triangle on its side. This is the run button.

For now you can only run SQL statements, but you also have other notebooks features such as different charting options and these are listed under the grey cell, where your SQL is located.

Here you can create Bar, Pie, Area, Line and Scatter charts. Here is an example of a Bar chart.

Warning: You do need to be careful of your syntax, as minimal details are given on what is wrong with your code. Not even the error numbers.

Go give it a good and see how far you can take these OML Notebooks.

Oracle Machine Learning notebooks

With the recent release of Oracle's Autonomous Data Warehouse Cloud (ADWC), Oracle has given data scientists a new tool for data discovery and machine learning on the ADWC. Oracle Machine Learning is based on Apache Zeppelin and gives us a new machine learning tool for accessing the in-database machine learning algorithms and in-database statistical functions.

Oracle Machine Learning (OML) SQL notebooks provide easy access to Oracle's parallelized, scalable in-database implementations of a library of Oracle Advanced Analytics' machine learning algorithms (classification, regression, anomaly detection, clustering, associations, attribute importance, feature extraction, times series, etc.), SQL, PL/SQL and Oracle's statistical and analytical SQL functions. Oracle Machine Learning SQL notebooks and Oracle Advanced Analytics' library of machine learning SQL functions combined with PL/SQL allow companies to automate their discovery of new insights, generate predictions and add "AI" to data viz dashboards and enterprise applications.

The key features of Oracle Machine Learning include:

• Collaborative SQL notebook UI for data scientists
• Packaged with Oracle Autonomous Data Warehouse Cloud
• Easy access to shared notebooks, templates, permissions, scheduler, etc.
• Access to 30+ parallel, scalable in-database implementations of machine learning algorithms
• SQL and PL/SQL scripting language supported
• Enables and Supports Deployments of Enterprise Machine Learning Methodologies in ADWC

Here is a list of key resources for Oracle Machine Learning:

• Oracle Machine Learning Notebooks
• Video overview of Oracle Machine Learning
• Download sample Oracle Machine Learning notebooks
• Quick Start Tutorial for getting started with Oracle Machine Learning
• Documentation: Using Oracle Machine Learning