intro to machine learning for oracle professionals - coug.us · intro to machine learning for...

Intro to Machine Learning for Oracle Professionals

Heli Helskyaho

@helifromfinland

Graduated from University of Helsinki (Master of Science, computer science), currently a doctoral student, researcher and lecturer (databases, Big Data, Multi-model Databases, methods and tools for utilizing semi-structured data for decision making) at University of Helsinki

Worked with Oracle products since 1993, worked for IT since 1990 Data and Database! CEO for Miracle Finland Oy Oracle ACE Director, Oracle Groundbreaker Ambassador Listed as one of the TOP 100 influences on IT sector in Finland (2015, 2016, 2017, 2018, 2019) Public speaker and an author Author of the book Oracle SQL Developer Data Modeler for Database Design Mastery (Oracle Press,

2015), co-author for Real World SQL and PL/SQL: Advice from the Experts (Oracle Press, 2016)

Introduction, Heli

What is machine learning?

An important part of Artificial Intelligence (AI)

Machine learning (ML) teaches computers to learn from experience(algorithms)

Arthur Samuel, 1959: “field of study that gives computers the ability to learn without being explicitly programmed“

A systematic study of algorithms and systems that improve theirknowledge or performance with experience

Why ML? Why now?

More and more data (volume)

Different data models and formats (variety)

Loading in progress while data exploration going on (velocity)

Not all data is reliable (veracity)

We do not know what we are looking for (value, viability, variability)

Must support also non-technical users (journalists, investors, politicians,…) (visualization)

All must be done efficiently and fast and as much as possibly by machines

-> MACHINE LEARNING?

It is now possible to use machine learning

Improved technology (GPU, TPU,…)

The price for storage solutions

Existing data sets

Pre-trained models

Research!

Why ML? Why now?

An environment that NEEDS ML and is finally able to really use it

Why ML? Why now?

You have data!

ML cannot be performed without data

Rules and equations are

Complex (image recognition)

Constantly changing (fraud detection)

The nature of the data changes and the program must adapt (today’sspam is tomorrow’s ham) (predicting shopping trends)

When to use ML?

Spam filters

Log filters (and alarms)

Data analytics

Image recognition

Speech recognition

Medical diagnosis

Robotics

Real life use cases for ML

Searches for patterns and trends

Sometimes useful, sometimes not

Predicts future outcomes

Sometimes correct, sometimes not

Learns new skills

Learning takes long, did it really learn what we wanted?

Approximation! A sophisticated guess!

Unsupervised learning

Supervised learning

Reinforcement learning

Machine learning

Learning from unlabeled input data by finding hidden patterns or intrinsic structures in that data

Unsupervised learning

don’t have a specific goal

the target variable is unknown

are not sure what information the data contains

want to reduce the features of your data as a preprocessing for supervised learning, dimentionality reduction

Used typically when you

Clustering

Anomaly detection

The target is known

Training a model on known input and output data to predict future outputs (remember that uncertainty is always involved)

Testing the model on known input and output data to compare the prediction to the known output

Supervised learning

Classification

are two methods of pattern identification

The difference lies in the fact that

classification uses predefined classes in which objects are assigned

clustering identifies similarities between objects, which it groups according to those characteristics in common and which differentiate them from other groups of objects. These groups are called "clusters". The number of clusters is a hyperparameter.

Classification and clustering

Regression

What is deep learning?

https://towardsdatascience.com/why-deep-learning-is-needed-over-traditional-machine-learning-1b6a99177063

Computer vision (Face recognition,…)

Speech recognition

Text recognition, text processing,…

Needs a LOT of data and resources

Datasets, pre-trained models, transfer learning,…

Deep Learning (DL)

Convolutional Neural Networks (CNN, orConvNet)

Filter

Pooling layers

Neural Network

Output0 (cat)1 (dog)

Output

Convolutional layers

Flattening

Inputs weights bias activation function output

(X1*w1)+(x2*w2)+b

∑ixiwi+b

Neurons

f(x)W2

Number of epochs? (One epoch is when an entire dataset is passed both forward and backward through the neural network once)

Batch size? (One epoch is divided into several smaller batches) Number of Hidden Layers and units? How many neurons in each layer? Weights? Bias? Activation function? Learning rate/step size: gradient descent? Optimizer? …

Model design, hyperparameters

The process for neural network to learn

Backpropagation/backward propagation

Layer(data

transformation)

Layer(data

transformarion)

Predictions Y’ True Targets Y

LossFunction

Loss Score

Optimizer

Weights

Input X

Weight update

Bias update

Text, voice,…

Natural Language Processing

Automatic speech recognition

Text to Speech

Machine Translation

Resume/summary of a text, …

Concerns: ”no need to learn to read anymore”

Use cases for deep learning?

https://talktotransformer.com/

https://deepai.org/machine-learning-model/text-generator

Translate

Recommender systems

produce suggestions/recommendations to assist their users in decision-making processes

Collaborative filtering (CF) recommender systems: recommendations based on decisions of other users with similar tastes

Content-based recommender systems: recommendations based on similarities of new items to those that the user liked in the past

Hybrid recommender systems: multiple approaches together

Amazon

Netflix

Visual recognizion problems, Computer vision:

Image classification

Object recognizion

Object detection

Image captioning

Action classification

Object/image segmentation (separate the object from its background)

https://towardsdatascience.com/everything-you-ever-wanted-to-know-about-computer-vision-heres-a-look-why-it-s-so-awesome-e8a58dfb641e

https://www.pyimagesearch.com/2020/06/01/opencv-social-distancing-detector/

The computer creates its own data

https://www.youtube.com/watch?v=VMp6pq6_QjI

What if data is not available?

310 000 attempts later: starting to be confident

Reinforcement learning (RL)

https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287

Very good with games

AlphaGo, AlphaGo Zero

https://deepmind.com/blog/article/alphago-zero-starting-scratch

BUT is it good with everything else?

Needs a lots of try and error…learns slower than humans…

How about teaching the car to park on a real parking lot? Maybe not…

Reinforcement learning (RL)

A lot!

Let’s see some examples…

What does Oracle offer for ML?

Oracle technologies for machine learning

Not part of the free offerings (but can be used with the credits)

There is also a desktop version: Oracle Analytics Desktop

Excellent tool for understanding the data and preparing it for ML

Oracle Analytics Cloud

Oracle Analytics Cloud (OAC)

Plenty of data source candidates

Project, data set, data flow, sequence

Prepare, Visualize, Narrate

OAC recommends enrichments and transformations to the data

augmented data enrichment sensitive data detection, geographic, or demographic enrichments

transformations dividing the name column into firstname and lastname columns

removing the social security number from the dataset

You can add your own transformations or enrichments

-> Preparation scripts

Prepare

Explain

Oracle Analytics Cloud

Create data flows

Save the result to a database

Visualize

Narrate

Use the ML models created with OML4SQL

Comes with the database

Free to use

OML4SQL

DBMS_PREDICTIVE_ANALYTICS

Routines for performing predictive analytics

DBMS_DATA_MINING_TRANSFORMING

Routines for transforming the data for mining models

DBMS_DATA_MINING

Routines for creating and managing mining models

OML4SQL

Can be used with any tool that can call PL/SQL packages

DBMS_PREDICTIVE_ANALYTICS.EXPLAIN(

data_table_name => 'beer_training_data',

explain_column_name => 'overall',

result_table_name => 'beer_explain');

OML4SQL

SQL Developer, GUI

OCI Data Science

Project

Notebook session

Notebook

Python library included into the Oracle Cloud Infrastructure Data Science service

offers a friendly user interface with objects and methods that describe the steps involved in the lifecycle of machine learning models

Oracle Accelerated Data Science (ADS) SDK

Exploratory data analysis

Automatic data visualization

Feature selection

Feature engineering

Algorithm selection

Feature encoding

Hyperparameter tuning

Model evaluation

Model interpretation and explainablility…

ADS, Oracle AutoML engine

ML is here now and it is the future

Oracle offers several tools for ML

Those tools can be used separately or together

Knowing Oracle technology gives you a headstart

It is all about good quality data

SQL, PL/SQL, R, Python, …

No excuses! Start learning, now!

Conclusions

THANK YOU!QUESTIONS?

Email: heli@miracleoy.fi

Twitter: @HeliFromFinland

Blog: Helifromfinland.com

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