4th International Conference On Recent Advances in Mathematical Sciences and Applications (RAMSA - 21) organized by GVP College of Engineering. This deck is an overview of the trends in ML Engineering which is evolving as a discipline and how Mathematics, Machine Learning and ML Engineering are related to one another.
A very high level introduction to the field of Data Science, Artificial Intelligence. Covers an introduction to Supervised Learning, Unsupervised Learning, Deep Learning and Neural Networks. Given as part of Industry Lectures event at GVP College of Engineering
PPT used during my speech during NASSCOM's BRAINS Event in Hyderabad, Sep 2019. This covers the emerging trends in Inferencing for Artificial Intelligence. PPT discusses about Edge Computing, VPUs, TPUs, GPUs etc.
International Journal of Artificial Intelligence and Soft Computing (IJAISC)MiajackB
International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.
My talk on AI for Human Resource Management at the Faculty Development Programme conducted by Department of Management Studies MVGR College of Engineering
Soumith Chintala at AI Frontiers: A Dynamic View of the Deep Learning WorldAI Frontiers
In this short talk, you will get an overview of Torch – a deep learning framework, and you will learn about how Torch offers certain valuable features for research that no other framework focuses on. You will also learn about new features introduced in a refreshed version of Torch.
4th International Conference On Recent Advances in Mathematical Sciences and Applications (RAMSA - 21) organized by GVP College of Engineering. This deck is an overview of the trends in ML Engineering which is evolving as a discipline and how Mathematics, Machine Learning and ML Engineering are related to one another.
A very high level introduction to the field of Data Science, Artificial Intelligence. Covers an introduction to Supervised Learning, Unsupervised Learning, Deep Learning and Neural Networks. Given as part of Industry Lectures event at GVP College of Engineering
PPT used during my speech during NASSCOM's BRAINS Event in Hyderabad, Sep 2019. This covers the emerging trends in Inferencing for Artificial Intelligence. PPT discusses about Edge Computing, VPUs, TPUs, GPUs etc.
International Journal of Artificial Intelligence and Soft Computing (IJAISC)MiajackB
International Journal of Artificial Intelligence and Soft Computing (IJAISC) is an open access peer-reviewed journal that provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Artificial Intelligence, Soft Computing. The Journal looks for significant contributions to all major fields of the Artificial Intelligence, Soft Computing in theoretical and practical aspects.
My talk on AI for Human Resource Management at the Faculty Development Programme conducted by Department of Management Studies MVGR College of Engineering
Soumith Chintala at AI Frontiers: A Dynamic View of the Deep Learning WorldAI Frontiers
In this short talk, you will get an overview of Torch – a deep learning framework, and you will learn about how Torch offers certain valuable features for research that no other framework focuses on. You will also learn about new features introduced in a refreshed version of Torch.
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning ResearchAI Frontiers
In this talk at AI Frontiers conference, Jeff Dean discusses recent trends and developments in deep learning research. Jeff touches on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. He gives an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
SkillsFuture Festival at NUS 2019- Machine Learning for HumansNUS-ISS
Presented by Mr Patrice Choong, Director, The Sandbox, Innovation & Entrepreneurship Office, Ngee Ann Polytechnic, at SkillsFuture Festival at NUS 2019
The Frontier of Deep Learning in 2020 and BeyondNUS-ISS
This talk will be a summary of the recent advances in deep learning research, current trends in the industry, and the opportunities that lie ahead.
We will discuss topics in research such as:
Transformers, GPT-3, BERT
Neural Architecture Search, Evolutionary Search
Distillation, self-learning
NeRF
Self-Attention
Also shifting industry trends such as:
The move to free data
Rising importance of 3D vision
Using synthetic data (Sim2Real)
Mobile vision & Federated Learning
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
This session explores the behind-the-scene experience of building an interactive gaming platform composed from a medley of technologies. The session starts with exploring the design thinking principles essential for creating engaging customer experience. Functional constructs provide parallelism, scalability and statelessness for gaming platform. The session elaborates such a functional programming perspective using Java. It explains the next level of sophistication by implementing a reactive stack for stream data processing. It also details interactive aspects of reactive game kernels and android console. The session finally explains use of Python based machine learning extensions incorporated to provide insights on player’s profile and games difficulties and popularity level.
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
Introduction to Machine Learning and Artificial Intelligence Technologies. Discover the basics surrounding this tech, including business uses and evolution over time.
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
SkillsFuture Festival at NUS 2019- Industrial Deep Learning and Latest AI Al...NUS-ISS
Presented by Dr Xavier Bresson, Associate Professor, School of Computer Science and Engineering, Nanyang Technological University, at SkillsFuture Festival at NUS 2019
My lecture during a Faculty Development Program on the discipline of Computer Vision. Covers a breadth of topics in the field of Computer Vision using both classical image processing algorithms and techniques to new approaches of deep learning etc.
Machine Learning 2 deep Learning: An IntroSi Krishan
Provides a brief introduction to machine learning, reasons for its popularity, a simple walk through example and then a need for deep learning and some of its characteristics. This is an updated version of an earlier presentation.
Jeff Dean at AI Frontiers: Trends and Developments in Deep Learning ResearchAI Frontiers
In this talk at AI Frontiers conference, Jeff Dean discusses recent trends and developments in deep learning research. Jeff touches on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. He gives an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.
SkillsFuture Festival at NUS 2019- Machine Learning for HumansNUS-ISS
Presented by Mr Patrice Choong, Director, The Sandbox, Innovation & Entrepreneurship Office, Ngee Ann Polytechnic, at SkillsFuture Festival at NUS 2019
The Frontier of Deep Learning in 2020 and BeyondNUS-ISS
This talk will be a summary of the recent advances in deep learning research, current trends in the industry, and the opportunities that lie ahead.
We will discuss topics in research such as:
Transformers, GPT-3, BERT
Neural Architecture Search, Evolutionary Search
Distillation, self-learning
NeRF
Self-Attention
Also shifting industry trends such as:
The move to free data
Rising importance of 3D vision
Using synthetic data (Sim2Real)
Mobile vision & Federated Learning
The purpose of this workshop was to highlight the the significance of AI, IoT and their integration under the light of scientific research. The presentation of the workshop can be found below.
This session explores the behind-the-scene experience of building an interactive gaming platform composed from a medley of technologies. The session starts with exploring the design thinking principles essential for creating engaging customer experience. Functional constructs provide parallelism, scalability and statelessness for gaming platform. The session elaborates such a functional programming perspective using Java. It explains the next level of sophistication by implementing a reactive stack for stream data processing. It also details interactive aspects of reactive game kernels and android console. The session finally explains use of Python based machine learning extensions incorporated to provide insights on player’s profile and games difficulties and popularity level.
Deep Learning for Data Scientists - Data Science ATL Meetup Presentation, 201...Andrew Gardner
Note: these are the slides from a presentation at Lexis Nexis in Alpharetta, GA, on 2014-01-08 as part of the DataScienceATL Meetup. A video of this talk from Dec 2013 is available on vimeo at http://bit.ly/1aJ6xlt
Note: Slideshare mis-converted the images in slides 16-17. Expect a fix in the next couple of days.
---
Deep learning is a hot area of machine learning named one of the "Breakthrough Technologies of 2013" by MIT Technology Review. The basic ideas extend neural network research from past decades and incorporate new discoveries in statistical machine learning and neuroscience. The results are new learning architectures and algorithms that promise disruptive advances in automatic feature engineering, pattern discovery, data modeling and artificial intelligence. Empirical results from real world applications and benchmarking routinely demonstrate state-of-the-art performance across diverse problems including: speech recognition, object detection, image understanding and machine translation. The technology is employed commercially today, notably in many popular Google products such as Street View, Google+ Image Search and Android Voice Recognition.
In this talk, we will present an overview of deep learning for data scientists: what it is, how it works, what it can do, and why it is important. We will review several real world applications and discuss some of the key hurdles to mainstream adoption. We will conclude by discussing our experiences implementing and running deep learning experiments on our own hardware data science appliance.
Introduction to Machine Learning and Artificial Intelligence Technologies. Discover the basics surrounding this tech, including business uses and evolution over time.
Data Workflows for Machine Learning - Seattle DAMLPaco Nathan
First public meetup at Twitter Seattle, for Seattle DAML:
http://www.meetup.com/Seattle-DAML/events/159043422/
We compare/contrast several open source frameworks which have emerged for Machine Learning workflows, including KNIME, IPython Notebook and related Py libraries, Cascading, Cascalog, Scalding, Summingbird, Spark/MLbase, MBrace on .NET, etc. The analysis develops several points for "best of breed" and what features would be great to see across the board for many frameworks... leading up to a "scorecard" to help evaluate different alternatives. We also review the PMML standard for migrating predictive models, e.g., from SAS to Hadoop.
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180804@Taiwan AI Academy, Hsinchu
6 hour lecture for those new to machine learning, to grasps the concepts, advantages and limitations of various classical machine learning methods. More importantly, to learn the skills to break down large complicated AI projects into manageable pieces, where features and functionalities could be added incrementally and annotated data accumulated. Take home message: machine learning is always a delicate balance between model complexity M and number of data N so that the trained classifier generalizes well and does not overfit.
SkillsFuture Festival at NUS 2019- Industrial Deep Learning and Latest AI Al...NUS-ISS
Presented by Dr Xavier Bresson, Associate Professor, School of Computer Science and Engineering, Nanyang Technological University, at SkillsFuture Festival at NUS 2019
My lecture during a Faculty Development Program on the discipline of Computer Vision. Covers a breadth of topics in the field of Computer Vision using both classical image processing algorithms and techniques to new approaches of deep learning etc.
Brief introduction to Digital Image Processing
Some common terminology such as Analog Image, Digital Image, Image Enhancement, Image Restoration, Segmentation
Introduction to computer vision with Convoluted Neural NetworksMarcinJedyk
Introduction to computer vision with Convoluted Neural Networks - going over history of CNNs, describing basic concepts such as convolution and discussing applications of computer vision and image recognition technologies
Machine learning for IoT - unpacking the blackboxIvo Andreev
Have you ever considered Machine Learning as a black box? It sounds as a kind of magic happening. Although being one among many solutions available, Azure ML has proved to be a great balance between flexibility, usability and affordable price. But how does Azure ML compare with the other ML providers? How to choose the appropriate algorithm? Do you understand the key performance indicators and how to improve the quality of your models? The session is about understanding the black box and using it for IoT workload and not only.
Overview of Computer Vision For Footwear IndustryTanvir Moin
Computer vision is an interdisciplinary field that focuses on enabling computers to interpret and analyze visual data from the world around us. It involves the development of algorithms and techniques that allow machines to understand images and videos, just as humans do.
The main goal of computer vision is to create machines that can "see" and understand the world around them, and then use that information to make decisions or take actions. This can involve tasks such as object recognition, scene reconstruction, facial recognition, and image segmentation.
Computer vision has a wide range of applications in various fields, such as healthcare, entertainment, transportation, robotics, and security. Some examples include medical image analysis, autonomous vehicles, augmented reality, and surveillance systems.
In recent years, the development of deep learning techniques, particularly convolutional neural networks (CNNs), has greatly advanced the field of computer vision, allowing machines to achieve state-of-the-art performance on various visual recognition tasks.
FACE RECOGNITION ACROSS NON-UNIFORM MOTION BLUR Koduru KrisHna
we will get the original image by giving the read command in the MAT LAB code. The remaining images are the illuminated image, blurred image, de-blurred image, illuminated blurred image which is modulated with the LBP technique, original image which is modulated with the LBP technique and the closest match gallery image. The closest match gallery image is obtained by comparing with all the images present in the database.
Chen Sagiv, co founder and co CEO of SagivTech, gave an introduction talk to Computer Vision at She Codes branch in Google Campus TLV.
In the talk an overview was given on what is computer vision, where it is used, some basic notions and algorithms and the AI revolution.
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Chapter8Hakky St
This is the documentation of the study-meeting in lab.
Tha book title is "Hands-On Machine Learning with Scikit-Learn and TensorFlow" and this is the chapter 8.
This is the slide deck I used when speaking at Praxis school of Business on the topic "Neural Networks -it’s usage in Corporate" as part of their Distinguished Speaker Series.
An introduction to usage of AI in Pharmaceutical industry. Machine Learning algorithms and use cases for Pharma Industry have been discussed in this PPT during the FDP for Andhra University
This PPT was used in the Faculty Development Program conducted by MUST Research. It explains the supervised machine learning algorithms of Classification and Learning to Rank and lists some crucial differences in their use cases.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
5. • Branch of Machine Learning which deals with
Images
• Unstructured Data
• Everywhere
• Captured from cameras
• Created by software like MSPaint, Coreldraw,
Adobe Photoshop etc
• Created by software like AutoCAD, Catia, Adobe
Acrobat, MS Word, Powerpoint
• Can contain text, regular shapes, irregular
shapes
• Contain a treasure of information
INTRODUCTIO
N TO
COMPUTER
VISION
7. COLOUR SPACES
RGB
Red, Green and Blue
0-255
CMYK Cyan, Magenta, Yellow and Key
HSV Hue, Saturation and Value
Grayscale Black and White
8. COLOUR SPACES
Traditional Colors
•Described by Isaac Newton described 1672.
•Primary colors are Red, Green and Blue
•Commonly referred to as "Painter's Colors“.
•Not all colors can be generated.
Subtractive Colors
•Called "Printer's Colors“.
•Colour we see is because of a particular frequency not being absorbed from White light. i.e. Subtracted
•Primary colors are Cyan, Yellow, and Magenta.
Additive Colors
•Adds primary colours together to get a choice of colour
•Displays work like this
9. WHAT IS IMAGE
PROCESSING?
• Extract quantifiable and meaningful
information out of an image
• Objects present in the image
• Location in the image
• Background or Foreground
• Distance from the viewer
10. IS IMAGE PROCESSING NEW TO COMPUTERS?
No. My grand mother used it without ever seeing a computer.
Remember the days before internet?
Features in a Cathode Ray Tube Television
Brightening
Contrast
Colour
Sharpness
How was this done?
Convolution
11. CONVOLUTION IN DIGITAL WORLD
• Process of adding each element of an image to its local neighbours weighted by a curve
• NOT the same as MatMult
• Used for blurring, sharpening, Up/Down sampling, Spherical distortion, De-noising, noise-filter etc
12. CONVOLUTION IN DIGITAL WORLD
• Depending on the convolution matrix, steps and operation chosen, he resultant image shall vary.
19. COMPUTER VISION – THE (AGE) OLD PROBLEMS
• What should a robot do in “Scene understanding”?
• Identify colours, brightness etc
• Identify objects a.k.a Image Segmentation
• Different things
• Multiple occurrences of the same thing
• Stuff other than things
• Distance of things and stuff
• Relative and absolute
20. COLOUR AND
BRIGHTNESS
Colour
spaces
•Grayscale,
RGB, CMY,
•Transparen
cy/Opacity
using a
fourth
attribute
Limitations
•Does not
represent all
colours in
nature
•colour
perception
highly
susceptible to
lighting
changes.
New Solutions
• Colour spaces
have been
expanded
greatly.
• With micro and
macro level
differences,
~250 colour
spaces are in
vogue
• HSV, HSL/HSI,
YUV, YPbPr,
YCbCr etc
22. OLD PROBLEM –
IMAGE
SEGMENTATION
Image is an matrix of numbers.
How to identify the edges of each object
How to recognize the object correctly
Differentiate between “things”
(foreground) and “stuff” (background)
23. IMAGE SEGMENTATION
–
OLD SOLUTIONS
Solution
Family
Algorithm Drawbacks
Thresholding
• Otsu thresholding
• Adaptive local thresholding
• Mean
• Gaussian
For reasonably simple scenarios only
Edges and Corners
• Canny edges, Sobel Hough, Laplace algorithms
• Harris Corner detection
• Convolution of kernels
Unsuitable for noisy/blurry images
Region Growing
Watershed
• Relatively strong at detecting overlapping/touching
objects
Super Pixels
• SLCI Algorithm
• Susceptible to noise
• Steep increase in algorithmic complexity
Clustering
• K-means
• Fuzzy C-Means (FCM)
• Expectation Maximization (EM)
• Relies on low level features like colour etc.
• Poor performance on complicated images
Clustering • Image Pyramid
• Carefully controlled environments only
• Cannot handle non-affine transformation like rotation,
reflection etc.
• Occlusions are a big no-no
• Compute intensive
24. IMAGE SEGMENTATION
–
CONVOLUTIONAL NEURAL NETWORKS
• Specialized kind of neural networks
• Process data in known grid-like spatial structures
• Comprised of large number of layers like convolution, pooling and Fully connected layers
• Usually, very very deep. i.e. lots of layers and lots of weight parameters
• Non linear Activation Functions are mandatory for learning complex features
26. EVOLUTIO
N OF CNN
CLASSIFIE
RS
2014
• Regions
with CNN
Features
2015
• Fast R-CNN
• Faster R-CNN
• Inception V3
2016
• YOLO
• SSD
• UberNet
2017
• Mask R-CNN
• Pixel wise
Instance
Segmentation
27. SOME
SALIENT
POINTS
Regions with CNN Features
R-CNN
•Uses Selective Search
•Significantly reduced the search space to ~2000 region proposal
•Very Slow and very complicated
Designed to solve the problems with R-CNN
Fast R-CNN
•Region Of Interest is treated as a pooling layer
•Jointly trains feature extractor, classifier and bounding box regression into a single model
•Almost 25 time faster than R-CNN
Replace Selective search with region proposal network
Faster R-CNN
•10 times faster than Fast R-CNN
You Only Look Once
YOLO
•Detection is considered as a regression problem
•Extremely fast but less accurate. Struggles with small objects that appear in groups
Single Shot Multi box detector
SSD
•Faster than YOLO and more accurate as well.
Extension of Faster R-CNN
Mask R-CNN
•Predicts the object masks as well as bounding box
•Impressive results
29. OLD
SOLUTIONS
-
DEPTH
PERCEPTIO
N
• Stereo cameras spaced at a fixed distance apart capture the
same image.
• Remember trigonometry?
• Algorithm Families
• Triangulation
• Interferometry
• Time of Flight
• Many Limitations
• Cost
• Complexity
• Controlled environments only
30. NEW
SOLUTIONS
-
DEPTH
PERCEPTIO
N
• Furious research in progress
• Single camera moving between two fixed positions
• Monocular Depth perception
• Some interesting proposals
• Train NN with depth information and semantically segmented
image
• Use the models for predicting depth in new images
• Solutions are almost mainstream
• Anyone heard of Kinect?
32. OLD PROBLEM
-
PROGRAMMERS
DILEMMA
• Which image format should I use?
• Which image file format should I code for? Do I have to learn
reading and writing image files?
• Matlab is expensive
33. NEW SOLUTION
-
OPENCV, PYTHON,
PILLOW ETC
• OpenCV
• Democratized image processing
• A large number of functionalities provided as APIs
• Impressive Python bindings and native support for C, Java
• Python
• PILLOW and many other libraries for reading images
• Vectorization and Numpy Arrays
35. NEURAL
NETWORKS
• Data hungry. Lots and lots of training data.
• Resource hungry and compute intensive.
• Overfitting, Underfitting, Stochasticity
• Black box
36. SOME
SOLUTIONS
• Transfer Learning to reduce training time
• Hyper parameter tuning
• Hardware based solutions for improving performance
• On-going research for explainability
• On-going research for reducing the training data requirement 3rd
generation neural networks