This document provides information about social media links, an introduction to artificial intelligence and machine learning, and modules for an AI and ML course. It includes Karan Shaw's social media links and background. It then defines AI as systems that mimic human behavior through understanding how humans think and learn. Machine learning is described as systems that can learn from experience without being explicitly programmed. Finally, it outlines 15 modules that will be covered in the course, including introductions to AI and ML, different AI techniques, supervised and unsupervised learning, and linear regression models.
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
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This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearnâs Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
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AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in todayâs day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Artificial Intelligence with Python | EdurekaEdureka!
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YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
This presentation discusses matters of AI and machine learning. This presentation was given during the ITU-T workshop on Machine Learning for 5G and beyond, held at ITU HQ in Geneva, Switzerland on 29 Jan 18. More information on the workshop can be found here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180129/Pages/default.aspx
Join our upcoming forums and workshops here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/Pages/default.aspx
Artificial Intelligence Course | AI Tutorial For Beginners | Artificial Intel...Simplilearn
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This Artificial Intelligence presentation will help you understand what is Artificial Intelligence, types of Artificial Intelligence, ways of achieving Artificial Intelligence and applications of Artificial Intelligence. In the end, we will also implement a use case on TensorFlow in which we will predict whether a person has diabetes or not. Artificial Intelligence is a method of making a computer, a computer-controlled robot or a software think intelligently in a manner similar to the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. Artificial Intelligence is emerging as the next big thing in the technology field. Organizations are adopting AI and budgeting for certified professionals in the field, thus the demand for trained and certified professionals in AI is increasing. As this new field continues to grow, it will have an impact on everyday life and lead to considerable implications for many industries. Now, let us deep dive into the AI tutorial video and understand what is this Artificial Intelligence all about and how it can impact human life.
The topics covered in this Artificial Intelligence presentation are as follows:
1. What is Artificial intelligence?
2. Types of Artificial intelligence
3. Ways of achieving artificial intelligence
4. Applications of Artificial intelligence
5. Use case - Predicting if a person has diabetes or not
Simplilearnâs Artificial Intelligence course provides training in the skills required for a career in AI. You will master TensorFlow, Machine Learning and other AI concepts, plus the programming languages needed to design intelligent agents, deep learning algorithms & advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems without explicit programming.
Why learn Artificial Intelligence?
The current and future demand for AI engineers is staggering. The New York Times reports a candidate shortage for certified AI Engineers, with fewer than 10,000 qualified people in the world to fill these jobs, which according to Paysa earn an average salary of $172,000 per year in the U.S. (or Rs.17 lakhs to Rs. 25 lakhs in India) for engineers with the required skills.
Those who complete the course will be able to:
1. Master the concepts of supervised and unsupervised learning
2. Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of machine learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
Comprehend the theoretic
Learn more at: https://www.simplilearn.com
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
Â
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck is loaded with easy-to-follow content, and intuitive design. Introduce the types and levels of artificial intelligence using the highly-effective visuals featured in this PPT slide deck. Showcase the AI-subfield of machine learning, as well as deep learning through our comprehensive PowerPoint theme. Represent the differences, and interrelationship between AI, ML, and DL. Elaborate on the scope and use case of machine intelligence in healthcare, HR, banking, supply chain, or any other industry. Take advantage of the infographic-style layout to describe why AI is flourishing in todayâs day and age. Elucidate AI trends such as robotic process automation, advanced cybersecurity, AI-powered chatbots, and more. Cover all the essentials of machine learning and deep learning with the help of this PPT slideshow. Outline the application, algorithms, use cases, significance, and selection criteria for machine learning. Highlight the deep learning process, types, limitations, and significance. Describe reinforcement training, neural network classifications, and a lot more. Hit download and begin personalization. Our AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete Deck are topically designed to provide an attractive backdrop to any subject. Use them to look like a presentation pro. https://bit.ly/3ngJCKf
Artificial Intelligence with Python | EdurekaEdureka!
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YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
This presentation discusses matters of AI and machine learning. This presentation was given during the ITU-T workshop on Machine Learning for 5G and beyond, held at ITU HQ in Geneva, Switzerland on 29 Jan 18. More information on the workshop can be found here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/20180129/Pages/default.aspx
Join our upcoming forums and workshops here: https://www.itu.int/en/ITU-T/Workshops-and-Seminars/Pages/default.aspx
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
An overlook of artificial intelligence with a quick insight of its definitions and know-hows.
History, applications and various usage of artificial intelligence in real life.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing..
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics ââŻAlso concerns how computational methods can aid the understanding of human language
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Machine Learning and Real-World ApplicationsMachinePulse
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This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
Machine Learning Techniques in Python Dissertation - PhdassistancePhD Assistance
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Machine Learning (ML) is a Programming Model which is quite good and faster. It helps in taking better decisions where domain knowledge is an important aspect. The Machine Learning models require some data and probable outputs if any and develop the program using the computer.
The most popular and significant field in the world of technology today is machine learning. Thus, there is varied and diverse support offered for Machine Learning in terms of frameworks and programming languages.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee known about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3dcke6F
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44â1143520021
India No: +91â4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
Machine Learning. What is machine learning. Normal computer vs ML. Types of Machine Learning. Some ML Object detection methods. Faster CNN, RCNN, YOLO, SSD. Real Life ML Applications. Best Programming Languages for ML. Difference Between Machine Learning And Artificial Intelligence. Advantages of Machine Learning. Disadvantages of Machine Learning
An overlook of artificial intelligence with a quick insight of its definitions and know-hows.
History, applications and various usage of artificial intelligence in real life.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing..
NLP is the branch of computer science focused on developing systems that allow computers to communicate with people using everyday language. Also called Computational Linguistics ââŻAlso concerns how computational methods can aid the understanding of human language
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Machine Learning and Real-World ApplicationsMachinePulse
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This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
Machine Learning Techniques in Python Dissertation - PhdassistancePhD Assistance
Â
Machine Learning (ML) is a Programming Model which is quite good and faster. It helps in taking better decisions where domain knowledge is an important aspect. The Machine Learning models require some data and probable outputs if any and develop the program using the computer.
The most popular and significant field in the world of technology today is machine learning. Thus, there is varied and diverse support offered for Machine Learning in terms of frameworks and programming languages.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee known about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3dcke6F
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44â1143520021
India No: +91â4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
AI and automation is all the rage nowadays - but whatâs the history of these technologies, innovations and ideas?
AI and automation is all the rage nowadays - but whatâs the history of these technologies, innovations and ideas? This slides will discuss the brief history of the current interesting technologies and their development to society and mankind.
Top 5 Machine Learning Tools for Software Development in 2024.pdfPolyxer Systems
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Machine learning has been widely used by various industries in 2023. The software development industry can take great advantage of machine learning in 2024 as well.
It has great potential to revolutionize various aspects of software development including task automation, boosting user experience, and easy software development and deployment.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
Python has a variety of libraries and frameworks, which makes it the best programming language of all time Letâs have a look at the top 11 Python frameworks for Machine learning and deep learning
HOW ARTIFICIAL INTELLIGENCE AND ITS SOFTWARES AND SMART ALGORITHMS WORK.pdfFaga1939
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This article aims to present how Artificial Intelligence, its software and its intelligent algorithms work, as well as the advantages and disadvantages of its use. Artificial intelligence (AI) is a computational technology developed with the aim of enabling machines to solve a series of problems, covering everything from the great complexity of government and industry management to the daily tasks of modern men and women. To do this, AI uses sophisticated learning technology, allowing the AI to learn from a large set of data and act on its own. Algorithms are the essence of any artificial intelligence system that are fed with as much data as possible, as references, so that they can learn better. It is important to note that unlike the algorithm, software is a type of system that allows the user to interact with the computer and gives instructions to the computer to perform specific tasks as well as control the functioning of the hardware and its operations. The advantages of using artificial intelligence include: 1) Reduction in human error; 2) Takes risks instead of human beings; 3) Availability of use (24 hours in 7 days); 4) Help with repetitive work; 5) Offers digital assistance; 6) Provides faster decisions; 7) Provides daily applications; 8) Promotes innovation. As a disadvantage, the use of artificial intelligence could cause machines to become so developed that humans will not be able to keep up with them and they will be able to continue on their own, redesigning themselves at an exponential rate, which could lead to invasion of people's privacy and even being turned into weapons and could lead to the extinction of the human race, in addition to promoting the advancement of unemployment, whether among manual workers or intellectual workers, because intelligent machines will also become workers.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Dev Dives: Train smarter, not harder â active learning and UiPath LLMs for do...UiPathCommunity
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đĽ Speed, accuracy, and scaling â discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Miningâ˘:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing â with little to no training required
Get an exclusive demo of the new family of UiPath LLMs â GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
đ¨âđŤ Andras Palfi, Senior Product Manager, UiPath
đŠâđŤ Lenka Dulovicova, Product Program Manager, UiPath
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
⢠The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
⢠Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
⢠Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
⢠Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more âmechanicalâ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as âpredictable inferenceâ.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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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/
1. Social Media Links
LinkedIn: https://www.linkedin.com/in/karan-shaw
Facebook: https://www.facebook.com/karan.shaw.37
Twitter: https://twitter.com/Karan26659889
Website: https://trybotics.com/about
Karan Shaw
Technical Lead of AI & ML @Nexright,
Founder of Trybotics(IOT & Robotics
Enthusiastic, YouTuber, Tech Trainer &
Speaker)
2.
3. Artificial Intelligence & Machine Learning
Artificial Intelligence (AI) is an area of computer science that emphasizes the
creation of intelligent machines that work and react like humans.
Machine learning is an application of artificial intelligence (AI) that provides systems the ability
to automatically learn and improve from experience without being explicitly programmed.
4. Module 1
1. Introduction to Artificial Intelligence
2. Applications of AI & Current trends
3. Different AI Techniques
4. AI Agents
5. PEAS Analysis
6. Agent Environment Analysis
7. Different Types of AI Agents
8. Machine Learning
9. Introduction and Applications of Machine
Learning
10.Supervised and Unsupervised Learning
11.Classification & Regression Problem
12.Clustering, Anomaly Detection
13.Getting started with Linear Regression
14.Mathematics behind Linear Regression
15.Building Linear Model
16.Gradient Descent Algorithm
17.Error Correction
5. Artificial Intelligence
What is Artificial Intelligence ?
Artificial Intelligence is nothing but the capability of a machine to imitate intelligent human behavior. AI is achieved
by mimicking a human brain, by understanding how it thinks, how it learns, decides, and work while trying to solve
a problem.
6.
7. Applications of AI & Current trends
AI has been dominant in various fields such as:
Gaming â AI plays crucial role in strategic games such
as chess, poker, tic-tac-toe, etc., where machine can
think of large number of possible positions based on
heuristic knowledge.
Natural Language Processing â It is possible to
interact with the computer that understands natural
language spoken by humans.
Expert Systems â There are some applications which
integrate machine, software, and special information to
impart reasoning and advising. They provide explanation
and advice to the users.
Vision Systems â These systems understand, interpret,
and comprehend visual input on the computer. For
example,A spying aeroplane takes photographs, which
are used to figure out spatial information or map of the
areas.
Speech Recognition â Some intelligent systems are
capable of hearing and comprehending the language in
terms of sentences and their meanings while a human
talks to it. It can handle different accents, slang words,
noise in the background, change in humanâs noise due to
cold, etc.
Handwriting Recognition â The handwriting recognition
software reads the text written on paper by a pen or on
screen by a stylus. It can recognize the shapes of the
letters and convert it into editable text.
Intelligent Robots â Robots are able to perform the
tasks given by a human. They have sensors to detect
physical data from the real world such as light, heat,
temperature, movement, sound, bump, and pressure.
8. Different AI Techniques
ďArtificial Neural Networks
Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way
in which the human brain processes information.
ďNatural Language Processing
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand,
interpret and manipulate human language.
ďNatural Language Understanding
Natural-language understanding or natural-language interpretation is a subtopic of natural-language
processing in artificial intelligence
ďSupport Vector Machines
âSupport Vector Machineâ (SVM) is a supervised machine learning algorithm which can be used for both
classification or regression challenges.
ďHeuristics
Heuristics are a problem-solving method that uses shortcuts to produce good-enough solutions given a
limited time frame or deadline.
9.
10.
11. Machine Learning and Itâs Applications
What is Machine Learning?
Machine Learning is a subset of Artificial Intelligence which provide computers with the ability to learn without
being explicitly programmed. In machine learning, we do not have to define explicitly all the steps or
conditions like any other programming application. The machine gets trained on a training dataset, large
enough to create a model, which helps machine to take decisions based on its learning.
Applications of Machine Learning
ďVirtual Personal Assistants (Google Assistants, Alexa, Siri)
ďSocial Media Services (Face Detection, Friend Suggestion, New Feed, Advertise)
ďEmail Spam and Malware Filtering (Gmail, Yahoo)
ďOnline Customer Support (Chatbot)
ďSearch Engine Result Refining (Google Search, Bing Search)
ďProduct Recommendations (Amazon.com, Flipkart.com)
ďPredictions while Commuting ( GPS navigation services)
16. Text to Features (Feature Engineering on text data)
â˘Syntactic Parsing (Grammar and Part of Speech tags)
â˘Entity Extraction (Entities as features)
â˘Statistical Features (Count / Density / Readability Features)
â˘Word Embedding (Text vectors)
Text Classification
17. Application of NLP
Text Summarization â Given a text article or paragraph, summarize it automatically to
produce most important and relevant sentences in order.
Machine Translation â Automatically translate text from one human language to another
by taking care of grammar, semantics and information about the real world, etc.
Natural Language Generation and Understanding â Convert information from computer
databases or semantic intents into readable human language is called language
generation. Converting chunks of text into more logical structures that are easier for
computer programs to manipulate is called language understanding.
Optical Character Recognition â Given an image representing printed text, determine the
corresponding text.
Document to Information â This involves parsing of textual data present in documents
(websites, files, pdfs and images) to analyzable and clean format.
18. Important NLP Libraries
â˘Scikit-learn: Machine learning in Python
â˘Natural Language Toolkit (NLTK): The complete toolkit for all NLP techniques.
â˘Pattern â A web mining module for the with tools for NLP and machine learning.
â˘TextBlob â Easy to use NLP tools API, built on top of NLTK and Pattern.
â˘spaCy â Industrial strength NLP with Python and Cython.
â˘Gensim â Topic Modelling for Humans
â˘Stanford Core NLP â NLP services and packages by Stanford NLP Group.
19. Module 2
1. Getting started with python programming.
2. Installing Anaconda
3. Python variables, lists, tuples and dictionaries.
4. Control Structure in Python.
5. Defining Functions in Python.
6. Using modules and packages.
7. NumPy for Data computation.
8. Matplotlib for Data Visualization.
9. Pandas for data exploration.
10.Using scikit-learn.
11.Creating linear regression models using scikit-learn.
20. python
⢠What is python?
Python is a programming language.
Python can be used on a server to create web applications.
⢠Download Python from - https://www.python.org/downloads
Check Python version: python --version
⢠Python is a popular programming language. It was created in 1991 by Guido van
Rossum. What can Python do?
1. Python can be used on a server to create web applications.
2. Python can be used alongside software to create workflows.
3. Python can connect to database systems. It can also read and modify files.
4. Python can be used to handle big data and perform complex mathematics.
5. Python can be used for rapid prototyping, or for production-ready software development.
21. python
ď First Python Program
⢠Interactive Mode Programming
>>> print (âHello, Python!â)
⢠Script Mode Programming.
1. Create test.py file and write the code-
print (âHello, Python!â) and save.
2. Then open command prompt and run the
command- python test.py
ď Why Python?
⢠Python works on different platforms (Windows, Mac,
Linux, Raspberry Pi, etc).
⢠Python has a simple syntax similar to the English
language.
⢠Python has syntax that allows developers to write
programs with fewer lines than some other programming
languages.
⢠Python runs on an interpreter system, meaning that code
can be executed as soon as it is written. This means that
prototyping can be very quick.
⢠Python can be treated in a procedural way, an object-
orientated way or a functional way.
ď Python is a case sensitive programming language.
E.g. Manpower and manpower are two different identifiers in
Python.
22. Installing Anaconda
⢠What is Anaconda?
Anaconda is a free and open-source distribution of the Python and R
programming languages for scientific computing, that aims to simplify package
management and deployment. Package versions are managed by the
package management system conda.
⢠Download Anaconda from ->
https://www.anaconda.com/distribution/#download-section
⢠What can Anaconda do?
1. Quickly download 1,500+ Python/R data science packages
2. Manage libraries, dependencies, and environments with Conda
3. Develop and train machine learning and deep learning models with scikit-learn andTensorFlow,
etc
23. Python Indentations And comments
ď Indentations
Where in other programming languages the indentation in
code is for readability only, in Python the indentation is
very important.
Python uses indentation to indicate a block of code.
E.g. -
if 5 > 2:
print ("Five is greater than two!")
ď Comments in Python
⢠Single Line Comment
# First Comment
print ("Hello, Python") # Second Comment
⢠Multi Line Comment
"""This is a
multiline docstring."""
print ("Hello, World!")
24. Python Variables
ď Example1:
x = 5
y = "John"
print(x)
print(y)
Output â
5
John
ď Example2:
x = "awesome"
print ("Python is " + x)
Output â Python is
awesome
ď Python Variables
Unlike other programming languages, Python has no
command for declaring a variable.
A variable is created the moment you first assign a value to
it. Python has a simple syntax similar to the English
language.
x = 4 # x is of type int
x = "Sally" # x is now of type str
print(x)
ď Rules for Python variables:
A variable name must start with a letter or the underscore
character.
A variable name cannot start with a number.
A variable name can only contain alpha-numeric characters
and underscores (A-z, 0-9, and _ ).
Variable names are case-sensitive (age, Age and AGE are
three different variables).
ď Python Numbers:
1. int
2. float
3. Complex
Output â
<class 'int'>
<class 'float'>
<class 'complexâ>
ď Example:
x = 1 # int
y = 2.8 # float
z = 1j # complex
print(type(x))
print(type(y))
print(type(z))
25. Python list, tuple, Set and dictionary
ď List:
List is a collection which is ordered and changeable. Allows
duplicate members. In Python lists are written with square
brackets.
Ex- ["apple", "banana", "cherry"]
ď Tuple:
Tuple is a collection which is ordered and unchangeable.
Allows duplicate members. In Python tuples are written with
round brackets.
Ex- ("apple", "banana", "cherry")
ď Set:
Set is a collection which is unordered and unindexed. No
duplicate members. In Python sets are written with curly
brackets.
Ex- {"apple", "banana", "cherry"}
ď Dictionary:
Dictionary is a collection which is unordered, changeable
and indexed. No duplicate members. In Python dictionaries
are written with curly brackets, and they have keys and
values.
Ex- { "brand": âMi", "model": "Note 7","year": 2019}
26. Control Structure in Python
ď Control Structure
⢠A set of actions define the flow of events as decided by
the flow chart.
⢠In general, statements are executed sequentially. The
first statement in a function is executed first, followed by
the second, and so on.
⢠There may be a situation when you need to execute a
block of code several number of times.
ď Loops in Python: In Python, there are three loops:
1. While
2. For
3. Nested
ď While Loop:
count = 0
while (count < 10):
print ( count )
count = count + 1
print ("Good bye!")
ď For Loop:
fruits = ['Banana', 'Apple', 'Grapes']
for index in range(len(fruits)):
print (fruits[index])
ď Nested Loop:
count = 1
for i in range(10):
print (str(i) * i)
for j in range(0, i):
count = count +1
27. Functions in Python
ď Functions
Functions are a convenient way to divide your code into useful blocks, allowing us to order our code, make it more
readable, reuse it and save some time.
Example:
def add (a, b):
return a + b
c = add(10,20)
print(c)
Output: 30
28. modules and packages
ď Module
A module is a piece of software that has a specific functionality. For example, when building a ping pong game, one
module would be responsible for the game logic, and
another module would be responsible for drawing the game on the screen. Each module is a different file, which can be
edited separately.
ď Package
Packages are namespaces which contain multiple packages and modules themselves. They are simply directories, but
with a twist.
Each package in Python is a directory which MUST contain a special file called __init__.py. This file can be empty, and
it indicates that the directory it contains is a Python package, so it can be imported the same way a module can be
imported.
29. NumPy for Data computation
ď NumPy
NumPy is a core library of Python for Data Science which stands for âNumerical Pythonâ. It is used for scientific
computing, which contains a powerful n-dimensional array object and provide tools for integrating C, C++ etc. It can
also be used as multi-dimensional container for generic data where you can perform various Numpy Operations and
special Functions.
ď NumPy Array: Numpy array is a powerful N-dimensional array object which is in the form of rows and columns. We can
initialize numpy arrays from nested Python lists and access it elements. In order to perform these numpy operations, the
next question which will come in your mind is:
ď How do I install NumPy?
To install Python NumPy, go to your command prompt and type âpip install numpyâ. Once the installation is completed,
go to your IDE (For example: PyCharm) and simply import it by typing: âimport numpy as npâ
Moving ahead in python numpy tutorial, let us understand what exactly is a multi-dimensional numPy array.
30. NumPy for Data computation
ď It is said to be two dimensional because it has rows as well as columns. In the above image, we have 3
columns and 4 rows available.
31. NumPy for Data computation
ď Python NumPy Array v/s List
We use python numpy array instead of a list because of the below three reasons:
1. Less Memory
2. Fast
3. Convenient
ď The very first reason to choose python numpy array is that it occupies less memory as compared to list.
Then, it is pretty fast in terms of execution and at the same time it is very convenient to work with
numpy. So these are the major advantages that python numpy array has over list.
ď Example:
import numpy as np
import time
import sys
S= range(1000)
print(sys.getsizeof(5)*len(S))
D= np.arange(1000)
print(D.size*D.itemsize)
32. NumPy for Data computation
ď Python NumPy Operations
1. ndim:
You can find the dimension of the array, whether it is a two-dimensional array or a single dimensional array. So, let
us see this practically how we can find the dimensions. In the below code, with the help of ândimâ function, I can
find whether the array is of single dimension or multi dimension.
import numpy as np
a = np.array([(1,2,3),(4,5,6)])
print(a.ndim)
2. itemsize:
You can calculate the byte size of each element. In the below code, I have defined a single dimensional array and
with the help of âitemsizeâ function, we can find the size of each element.
import numpy as np
a = np.array([(1,2,3)])
print(a.itemsize)
33. NumPy for Data computation
ď Python NumPy Operations
1. dtype:
You can find the data type of the elements that are stored in an array. So, if you want to know the data type of a
particular element, you can use âdtypeâ function which will print the datatype along with the size. In the below code, I
have defined an array where I have used the same function.
import numpy as np
a = np.array([(1,2,3)])
print(a.dtype)
2. reshape:
Reshape is when you change the number of rows and columns which gives a new view to an object. Now, let us
take an example to reshape the array:
34. NumPy for Data computation
ď Other Operations
You can perform more operations on NumPy array i.e. addition, subtraction, multiplication and division of the two
matrices.
ď Example:
import numpy as np
x= np.array([(1,2,3),(3,4,5)])
y= np.array([(1,2,3),(3,4,5)])
print(x+y) # addition
print(x-y) # subtraction
print(x*y) # multiplication
print(x/y) # division
35. Matplotlib for Data Visualization
ď Matplotlib
Matplotlib is a plotting library used for 2D graphics in python programming language. It can be used in
python scripts, shell, web application servers and other graphical user interface toolkits.
ď Install Matplotlib: pip install matplotlib
ď There are several sstoolkits which are available and that extend python matplotlib functionality
1. Basemap: It is a map plotting toolkit with various map projections, coastlines and political boundaries.
2. Cartopy: It is a mapping library featuring object-oriented map projection definitions, and arbitrary point, line,
polygon and image transformation capabilities.
3. Excel tools: Matplotlib provides utilities for exchanging data with Microsoft Excel.
4. Mplot3d: It is used for 3-D plots.
5. Natgrid: It is an interface to the natgrid library for irregular gridding of the spaced data.
36. Matplotlib for Data Visualization
ď let me show you very basic codes in python matplotlib in order to generate a simple graph:
demo1.py
ď Types of Plots:
37. Pandas for data exploration
ď Pandas
Pandas is an important library in Python for data science. It
is used for data manipulation and analysis. It is well suited
for different data such as tabular, ordered and unordered
time series, matrix data etc.
ď How to install Pandas?
1. Pip: pip install pandas
2. Anaconda: conda install pandas
ďś Pandas is build on the top of NumPy
and Matplotlib
ď Pandas Operations
Using Python pandas, you can perform a lot of operations
with series, data frames, missing data, group by etc.
38. Pandas for data exploration
ď In order to perform slicing on data, you need a data frame. Donât worry, data frame is a 2-dimensional data
structure and a most common pandas object. So first, letâs create a data frame.
import pandas as pd
XYZ_web= {'Day':[1,2,3,4,5,6], "Visitors":[1000, 700,6000,1000,400,350], "Bounce_Rate":[20,20, 23,15,10,34]}
df= pd.DataFrame(XYZ_web)
print(df)
39. Use Case to Analyse Youth Unemployment Data
ď Problem Statement:
You are given a dataset which comprises of the percentage of unemployed youth globally from 2010 to 2014. You have to use
this dataset and find the change in the percentage of youth for every country from 2010-2011.
40. scikit-learn
ď Scikit-Learn
Scikit learn is a library used to perform machine learning in Python. Scikit learn is an open source library which is
licensed under BSD and is reusable in various contexts, encouraging academic and commercial use. It provides a
range of supervised and unsupervised learning algorithms in Python. Scikit learn consists popular algorithms and
libraries. Apart from that, it also contains the following packages:
1. NumPy
2. Matplotlib
3. SciPy (Scientific Python)
ďś SciPy is a free and open-source Python library used for scientific computing and technical computing.
ďś Installation: pip install scipy
ď Learning and Predicting
Next, in Scikit learn, we have used a dataset (sample of 10 possible classes, digits from zero to nine) and we need to
predict the digits when an image is given. To predict the class, we need an estimator which helps to predict the classes
to which unseen samples belong. In Scikit learn, we have an estimator for classification which is a python object that
implements the methods fit(x,y) and predict(T).
41. Creating linear regression models using scikit-learn
ď This example uses
The only the first feature of the diabetes dataset, in order to illustrate a two-dimensional plot of this regression
technique. The straight line can be seen in the plot, showing how linear regression attempts to draw a straight line that
will best minimize the residual sum of squares between the observed responses in the dataset, and the responses
predicted by the linear approximation.
ď The coefficients, the residual sum of squares and the variance score are also calculated.
42. Module 3
1. Getting Started with Artificial Neural Networks
2. Introduction to neurons, weights
3. Activation Function
4. Input Layers, Hidden Layers and Output Layers
5. Single layer perceptron Model
6. Multilayer Neural Network
7. Back Propagation Algorithm introduction
8. Programming Neural Network using Python
9. Building Regression models using ANN
10.Classification Examples using ANN
43. Artificial Neural Networks
What is Artificial Neural Network?
An artificial neuron network (ANN) is a
computational model based on the structure
and functions of biological neural networks.
Information that flows through the network
affects the structure of the ANN because a
neural network changes - or learns, in a
sense - based on that input and output.
ANNs are considered nonlinear statistical
data modeling tools where the complex
relationships between inputs and outputs are
modeled or patterns are found.
44. Artificial Neural Networks
Neural Networks consist of the
following components:
ďAn input layer, x
ďAn arbitrary amount of hidden layers
ďAn output layer, š
ďA set of weights and biases between
each layer, W and b
ďA choice of activation function for
each hidden layer, Ď. In this tutorial,
weâll use a Sigmoid activation function.
45. Module 4
1. K Nearest Neighbour Models
2. Using KNN for Data Classification
3. Building Models using KNN
4. Support Vector Machine â Applications and Mathematics
5. Using SVM for classification
6. Projects:
a) Character Recognition Algorithm
b) Cancer Diagnostic Algorithm
c) Iris Clustering
46. K Nearest Neighbour Models
What is K-Nearest Neighbour ?
K- Nearest Neighbors or also known as K-NN belong to the family of supervised machine learning
algorithms which means we use labeled (Target Variable) dataset to predict the class of new data point.
The K-NN algorithm is a robust classifier which is often used as a benchmark for more complex classifiers
such as Artificial Neural Network (ANN) or Support vector machine (SVM).
47. Support Vector Machine
Support Vector Machine?
Support Vector Machineâ (SVM) is a supervised machine learning algorithm which can be used for
both classification or regression challenges. However, it is mostly used in classification problems. In
this algorithm, we plot each data item as a point in n-dimensional space (where n is number of
features you have) with the value of each feature being the value of a particular coordinate. Then,
we perform classification by finding the hyper-plane that differentiate the two classes very well (look at
the below snapshot).