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Videos uploaded by user “Machine Learning with Oracle”
How connect to an Oracle database from Python
 
08:52
Learn how to connect to an Oracle database from Python. Includes installation of packages, OS dependencies, connection configuration and troubleshooting.
Machine Learning with Oracle
 
35:47
Introduction - 0:00 Overview Machine Learning in Oracle - 1:31 Machine Learning theory - 6:04 Demonstration: preparation and building the model - 11:52 Demonstration: run the prediction and adapt the application - 26:24 How to get started - 33:33 Without a doubt Machine Learning / Artificial Intelligence is an incredibly powerful technology with a huge potential. It brings benefits across many industries and business functions: From better targeting in the marketing/sales domain to predictive maintenance in manufacturing. This video-webinar is a kickstart to Machine Learning. You will learn the required theoretical knowledge and then we'll go through a real-life example: intelligent sales with ML. We'll create our very first ML model, and use it to make an existing application intelligent with sales recommendations. After this webinar you will have the basic ingredients to apply ML to your own business cases! Note that you don't require any previous knowledge of ML to be able to understand this session. Powerpoint and background material can be found here: https://ptdrv.linkedin.com/cmaj4xt
Deploy Machine Learning Models (TensorFlow/Caffe2/ONNX) - Fast and Easy
 
10:00
In this video you learn how to Build and Deploy an Image Classifier with TensorFlow and GraphPipe. You can use the same technique to deploy models of other frameworks, such as Caffe2 and ONNX.
Oracle Autonomous Datawarehouse Cloud - Machine Learning with Oracle - Forecasting example
 
32:29
Part 1 00:00 Welcome 00:26 Agenda 01:31 Example use case and problem statement 03:48 Data sources we will use 04:43 Machine learning theory - The "Naive" Model 05:28 Machine learning theory - Supervised Learning 08:00 Introduction to Autonomous Datawarehouse Cloud 09:24 Data collection - introduction 10:23 Data collection - DataSync demo Part 2 11:33 Create prediction model with ADWC - Introduction 12:28 Create prediction model with ADWC - Combine the sources 15:08 Create prediction model with ADWC - Feature engineering 16:46 Create prediction model with ADWC - Validation theory 18:48 Create prediction model with ADWC - Syntax to create the model 21:35 Create prediction model with ADWC - Actual validation, visual approach 24:02 Create prediction model with ADWC - Actual validation, numerical approach Part 3 26:08 Running the prediction model - A notebook to predict tomorrow's sales 27:07 Running the prediction model with ADWC - Automatically run it every day 27:22 Running the prediction model with ADWC - Embedding in an (APEX) application 29:05 Conclusions 31:23 How to get started 31:53 Questions and contact When it comes to forecasting accuracy, machine Learning often outperforms the traditional models such as ARIMA. In this video we show you step-by-step how to use the power of machine learning for forecasting sales/demand. This video is also a good way to learn how to work with machine learning in Autonomous Datawarehouse Cloud in general. The principles you learn here can be applied to many more machine learning use cases. We'll also cover any required ML theory, so you don't require any previous knowledge on this. After watching this video you will have the basic ingredients to apply ML to your own business cases with Autonomous Datawarehouse Cloud.
Data Visualization for Oracle Application Express - Summary
 
05:40
Full length version of the video (45 minutes) is here: http://www.webinar123.biz/apex_full_video
NVidia GPU Cloud on Oracle Cloud Infrastructure - Massive GPU acceleration for deep learning!
 
07:41
I ran an experiment with a very performance-demanding deep learning process to see if Oracle's GPU servers can speed things up. In the process I learned about cloud architecture and painted a few pieces of art along the way. Check it out! Detailed steps to create your own free cloud GPU server: https://bit.ly/gpucloudinstructions Source code for the Style Transfer test case. Credits and many thanks to Anish Athalye. https://github.com/anishathalye/neural-style Get your own free Cloud trial here: https://bit.ly/tryoraclecloud
Linking ALL data: How Big Data helps solve and prevent crime
 
42:37
In 45m we will show you a practical demo that shows how to: - Perform complex criminal investigation and analysis: Detect, investigate and solve crime through link analysis by connecting people, companies, places and things. - Use ALL available information, such as financial transactions, criminal records, mobile phone records, telemetry, et cetera. We'll show you how to extract relevant information from these datasources, e.g. by detecting faces from photos and identifying key information in textual documents. - Detect patterns in criminal behaviour and respond quickly by automatically flagging suspicious activity. As an example, this demonstration will go through a case of a money laundering investigation. We'll go through the full process of data acquisition, analysis and conclusion.
Brick-and-mortar Retail Innovations with Internet of Things and Machine Learning
 
30:50
Introduction - 0:00 Demonstration part 1: Measure and act - 2:18 Demonstration part 2: Machine learning - 15:16 Demonstration part 3: Conclusion - 19:16 Wonderstore background - 20:20 Analytics cases in Retail - 27:03 In 30m we will demystify how Machine Learning and the Internet of Things work in Retail by going through a real demo that show you how to: * increase the window conversion rate (how well does the storefront draw in shoppers) * increase shop entrance to sale conversion rate * lower operational running cost * plan your staffing better We'll focus especially on brick-and-mortar retailers, who traditionally have found it difficult to collect information on their customers. Loyalty programs have helped to some extent, but most shop visitors don’t sign up for them, leaving the retailer "blind" for the preferences of their customers. New technological innovations such as intelligent cameras allow us to collect relevant information from our shop visitors. Think about data points such as customer age and gender. Combined with smart data analytics and machine learning, this gives us the capability to really understand our customer’s behaviour and give him/her the best offers and service. The Powerpoint can be found here: https://ptdrv.linkedin.com/48fllpl

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