It’s long ago, approx. 30 years, since AI was not only a topic for Science-Fiction writers, but also a major research field surrounded with huge hopes and investments. But the over-inflated expectations ended in a subsequent crash and followed by a period of absent funding and interest – the so-called AI winter. However, the last 3 years changed everything – again. Deep learning, a machine learning technique inspired by the human brain, successfully crushed one benchmark after another and tech companies, like Google, Facebook and Microsoft, started to invest billions in AI research. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new Hype? How is Deep Learning different from previous approaches? Are the advancing AI technologies really a threat for humanity? Let’s look behind the curtain and unravel the reality. This talk will explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why "Deep Learning is probably one of the most exciting things that is happening in the computer industry” (Jen-Hsun Huang – CEO NVIDIA).
Either a new AI “winter is coming” (Ned Stark – House Stark) or this new wave of innovation might turn out as the “last invention humans ever need to make” (Nick Bostrom – AI Philosoph). Or maybe it’s just another great technology helping humans to achieve more.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Artificial Intelligence, Machine Learning, Deep Learning
The 5 myths of AI
Deep Learning in action
Basics of Deep Learning
NVIDIA Volta V100 and AWS P3
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...Simplilearn
This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Artificial Intelligence, Machine Learning, Deep Learning
The 5 myths of AI
Deep Learning in action
Basics of Deep Learning
NVIDIA Volta V100 and AWS P3
Learn the fundamentals of Deep Learning, Machine Learning, and AI, how they've impacted everyday technology, and what's coming next in Artificial Intelligence technology.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
The next phase of Smart Network Convergence could be putting Deep Learning systems on the Internet. Deep Learning and Blockchain Technology might be combined in the smart networks of the future for automated identification (deep learning) and automated transaction (blockchain). Large scale future-class problems might be addressed with Blockchain Deep Learning nets as an advanced computational infrastructure, challenges such as million-member genome banks, energy storage markets, global financial risk assessment, real-time voting, and asteroid mining.
Blockchain Deep Learning nets and Smart Networks more generally are computing networks with intelligence built in such that identification and transfer is performed by the network itself through sophisticated protocols that automatically identify (deep learning), and validate, confirm, and route transactions (blockchain) within the network.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
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 today’s 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
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...Edureka!
This Edureka "What is Deep Learning" video will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
To take a structured training on Deep Learning, you can check complete details of our Deep Learning with TensorFlow course here: https://goo.gl/VeYiQZ
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
The next phase of Smart Network Convergence could be putting Deep Learning systems on the Internet. Deep Learning and Blockchain Technology might be combined in the smart networks of the future for automated identification (deep learning) and automated transaction (blockchain). Large scale future-class problems might be addressed with Blockchain Deep Learning nets as an advanced computational infrastructure, challenges such as million-member genome banks, energy storage markets, global financial risk assessment, real-time voting, and asteroid mining.
Blockchain Deep Learning nets and Smart Networks more generally are computing networks with intelligence built in such that identification and transfer is performed by the network itself through sophisticated protocols that automatically identify (deep learning), and validate, confirm, and route transactions (blockchain) within the network.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
AI Vs ML Vs DL PowerPoint Presentation Slide Templates Complete DeckSlideTeam
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 today’s 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
This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
What is Deep Learning | Deep Learning Simplified | Deep Learning Tutorial | E...Edureka!
This Edureka "What is Deep Learning" video will help you to understand about the relationship between Deep Learning, Machine Learning and Artificial Intelligence and how Deep Learning came into the picture. This tutorial will be discussing about Artificial Intelligence, Machine Learning and its limitations, how Deep Learning overcame Machine Learning limitations and different real-life applications of Deep Learning.
Below are the topics covered in this tutorial:
1. What Is Artificial Intelligence?
2. What Is Machine Learning?
3. Limitations Of Machine Learning
4. Deep Learning To The Rescue
5. What Is Deep Learning?
6. Deep Learning Applications
To take a structured training on Deep Learning, you can check complete details of our Deep Learning with TensorFlow course here: https://goo.gl/VeYiQZ
"Mainstream access to deep learning technology will greatly impact most industries over the next three to five years."
So what exactly is deep learning? How does it work? And most importantly, why should you even care?
Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing.
Practical examples include:
-Vehicle, pedestrian and landmark identification for driver assistance
-Image recognition
-Speech recognition and translation
-Natural language processing
-Life sciences
-What You Will Learn
-Understand the intuition behind Artificial Neural Networks
-Apply Artificial Neural Networks in practice
-Understand the intuition behind Convolutional Neural Networks
-Apply Convolutional Neural Networks in practice
-Understand the intuition behind Recurrent Neural Networks
-Apply Recurrent Neural Networks in practice
-Understand the intuition behind Self-Organizing Maps
-Apply Self-Organizing Maps in practice
-Understand the intuition behind Boltzmann Machines
-Apply Boltzmann Machines in practice
-Understand the intuition behind AutoEncoders
-Apply AutoEncoders in practice
by Dan Romuald Mbanga, Business Development Manager, AWS
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we will provide an overview of deep learning focusing on getting started with the TensorFlow and Keras frameworks on AWS. Level 100
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
Deep learning is a part of machine learning, which involves the use of computer algorithms to learn, improve and evolve on its own. Deep learning may be considered similar to machine learning. However, while machine learning works with simple concepts, deep learning uses artificial neural networks, which imitate the way humans learn and think.
This is an introduction to deep learning presented to Plymouth University students. In the introduction it is explained how a neural network works. In the practical section it is shown how to use Tensorflow for building simple models. Finally the case studies, how to use deep learning in real world applications.
Traditional Machine Learning had used handwritten features and modality-specific machine learning to classify images, text or recognize voices. Deep learning / Neural network identifies features and finds different patterns automatically. Time to build these complex tasks has been drastically reduced and accuracy has exponentially increased because of advancements in Deep learning. Neural networks have been partly inspired from how 86 billion neurons work in a human and become more of a mathematical and a computer problem. We will see by the end of the blog how neural networks can be intuitively understood and implemented as a set of matrix multiplications, cost function, and optimization algorithms.
Understanding Deep Learning & Parameter Tuning with MXnet, H2o Package in RManish Saraswat
Simple guide which explains deep learning and neural network with hands on experience in R using MXnet and H2o package. It also explains gradient descent and backpropagation algorithm.
Complete tutorial: http://blog.hackerearth.com/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-r
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
You have developed your application and are now facing the biggest challenge known to mankind: How to get and engage users?
This presentation will give an overview of various actionable growth hacking techniques based on our observations and experiences with mobile apps. Listen to us and go out to scale your app from zero to hero!
Topics covered: PlayStore Optimization, Social Media, Invitation and Referral Systems, Event and E-Mail Marketing, Analytics
Trend detection and analysis on TwitterLukas Masuch
By Henning Muszynski, Benjamin Räthlein & Lukas Masuch
The popularity of social media services has increased exponentially in the last few years. The combination of big social data and powerful analytical technologies makes it possible to gain highly valuable insights that otherwise might not be accessible. The Twitter Analyzer comprises several components to collect, analyze and visualize Twitter data. Therefore, we explored various related technologies to implement this tool. We collected about 38 million english tweets related to various and analyzed those data with machine learning techniques to compute the respective sentiment and detect common topics. Furthermore, we visualized the results using varying visualization techniques to emphasize different aspects such as a wordcloud, several chart-types and geospatial visualizations. Used technologies: MongoDB, Python, Twython, Python NLTK, wordcloud2.js, wordfreq, amCharts, Google BigQuery, Google Cloud Storage, CartoDB, EtcML.
Decision-making on approval and budgeting processes for new projects tends to be top-down in most companies. In contrast, crowdfunding uses the "wisdom of the crowd" to make funding decisions and is successfully used in the public scope (e.g. Kickstarter). SparkX is an enterprise crowdfunding platform that adapts this successful crowdfunding model to leverage its benefits in the enterprise context. This platform complements the traditional top-down decision-making to allow employees to collaborate on funding breakthrough ideas. SparkX helps to engage employees, promotes open innovation, revitalizes the culture intrapreneurship and sparks collaborative innovation from the bottom up.
Google Cloud Platform - Building a scalable mobile applicationLukas Masuch
In this presentation we give an overview on several services of the Google Cloud Platform and showcase an Android application utilizing these technologies. We cover technologies, such as Google App Engine, Cloud Endpoints, Cloud Storage, Cloud Datastore and Google Cloud Messaging (GCM). We will talk about pitfalls, show meaningful code examples (in Java) and provide several tips and dev tools on how to get the most out of Google’s Cloud Platform.
Inspired and Ideas by:
Death by PowerPoint (http://www.slideshare.net/thecroaker/death-by-powerpoint)
You Suck At PowerPoint (http://www.slideshare.net/jessedee/you-suck-at-powerpoint)
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
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 “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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.
3. Hype or Reality?
Quotes
I have worked all my life in Machine Learning, and I’ve never seen one
algorithm knock over benchmarks like Deep Learning
– Andrew Ng (Stanford & Baidu)
Deep Learning is an algorithm which has no theoretical limitations
of what it can learn; the more data you give and the more
computational time you provide, the better it is – Geoffrey Hinton (Google)
Human-level artificial intelligence has the potential to help humanity
thrive more than any invention that has come before it – Dileep George
(Co-Founder Vicarious)
For a very long time it will be a complementary tool that human
scientists and human experts can use to help them with the things
that humans are not naturally good – Demis Hassabis (Co-Founder DeepMind)
6. What is Artificial Intelligence?
Artificial
Intelligence
Output:
Movement
Text
Input:
Sensors
Data
7. Machine Learning - Basics
Introduction
Machine Learning is a type of Artificial Intelligence that provides
computers with the ability to learn without being explicitly
programmed.
Machine Learning
Algorithm
Learned Model
Data
Prediction
Labeled Data
Training
Prediction
Provides various techniques that can learn from and make predictions on data
8. Machine Learning - Basics
Learning Approaches
Supervised Learning: Learning with a labeled training set
Example: email spam detector with training set of already labeled emails
Unsupervised Learning: Discovering patterns in unlabeled data
Example: cluster similar documents based on the text content
Reinforcement Learning: learning based on feedback or reward
Example: learn to play chess by winning or losing
11. What is Deep Learning?
Part of the machine learning field of learning representations of
data. Exceptional effective at learning patterns.
Utilizes learning algorithms that derive meaning out of data by using
a hierarchy of multiple layers that mimic the neural networks of our
brain.
If you provide the system tons of information, it begins to
understand it and respond in useful ways.
12. Inspired by the Brain
The first hierarchy of neurons
that receives information in the
visual cortex are sensitive to
specific edges while brain regions
further down the visual pipeline
are sensitive to more complex
structures such as faces.
Our brain has lots of neurons connected together and the strength of
the connections between neurons represents long term knowledge.
One learning algorithm hypothesis: all significant mental algorithms
are learned except for the learning and reward machinery itself.1
14. A brief History
A long time ago…
1974 Backpropagation
awkward silence (AI Winter)
1995
SVM reigns
Convolution Neural Networks for
Handwritten Recognition
1998
2006
Restricted
Boltzmann
Machine
1958 Perceptron
1969
Perceptron criticized
Google Brain Project on
16k Cores
2012
2012
AlexNet wins
ImageNet
15. A brief History
The Big Bang aka “One net to rule them all”
ImageNet: The “computer vision World Cup”
16. A brief History
The Big Bang aka “One net to rule them all”
Deep Learning in Speech Recognition
17. What changed?
Old wine in new bottles
Big Data
(Digitalization)
Computation
(Moore’s Law, GPUs)
Algorithmic
Progress
18. The Big Players
Superstar Researchers
Geoffrey Hinton: University of Toronto & Google
Yann LeCun: New York University & Facebook
Andrew Ng: Stanford & Baidu
Yoshua Bengio: University of Montreal
Jürgen Schmidhuber: Swiss AI Lab & NNAISENSE
21. Deep Learning - Basics
No more feature engineering
Feature
Engineering
Traditional
Learning
AlgorithmInput Data
Costs lots of time
Deep
Learning
AlgorithmInput Data
22. Deep Learning - Basics
Architecture
A deep neural network consists of a hierarchy of layers, whereby each layer
transforms the input data into more abstract representations (e.g. edge ->
nose -> face). The output layer combines those features to make predictions.
23. Deep Learning - Basics
What did it learn?
Edges Nose, Eye… Faces
24. Deep Learning - Basics
Artificial Neural Networks
Consists of one input, one output and multiple fully-connected hidden layers in-
between. Each layer is represented as a series of neurons and progressively extracts
higher and higher-level features of the input until the final layer essentially makes a
decision about what the input shows. The more layers the network has, the higher-
level features it will learn.
25. Deep Learning - Basics
The Neuron
An artificial neuron contains a nonlinear activation function and has
several incoming and outgoing weighted connections.
Neurons are trained to filter and detect specific features or patterns
(e.g. edge, nose) by receiving weighted input, transforming it with
the activation function und passing it to the outgoing connections.
26. Deep Learning - Basics
Non-linear Activation Function
Non-linearity is needed to learn complex (non-linear)
representations of data, otherwise the NN would be just
a linear function.
Most deep networks use ReLU -
max(0,x) - nowadays for hidden
layers, since it trains much faster, is
more expressive than logistic
function and prevents the gradient
vanishing problem.
27. Deep Learning - Basics
The Training Process
Forward it trough
the network to get
predictionsSample labeled data
Backpropagate
the errors
Update the
connection weights
Learns by generating an error signal that measures the difference between the
predictions of the network and the desired values and then using this error signal
to change the weights (or parameters) so that predictions get more accurate.
28. Deep Learning - Basics
Gradient Descent
Gradient Descent finds the (local) the minimum of the cost function (used to
calculate the output error) and is used to adjust the weights.
29. Deep Learning - Basics
Data transformation in other dimensions
A neural network is transforming the data into other dimensions to solve
the specified problem.
30. Deep Learning - Basics
Deep Autoencoders
Composed of two symmetrical
deep-belief networks. The encoding
network learns to compresses the
input to a condensed vector
(dimensionality reduction). The
decoding network can be used to
reconstruct the data.
Topic Modeling: Document in a collection is converted to a Bag-of-
Words and transformed to a compressed feature vector using an
autoencoder. The distance from every other document-vector can be
measured and nearby document-vectors fall under the same topic.
31. Deep Learning - Basics
Convolutional Neural Nets (CNN)
Convolutional Neural Networks learn a complex representation of visual data
using vast amounts of data. They are inspired by the human visual system and
learn multiple layers of transformations, which are applied on top of each other
to extract a progressively more sophisticated representation of the input.
Every layer of a CNN takes a 3D volume of numbers and outputs a 3D volume of
numbers. E.g. Image is a 224*224*3 (RGB) cube and will be transformed to
1*1000 vector of probabilities.
32. Deep Learning - Basics
Convolutional Neural Nets (CNN)
Convolution layer is a feature detector that automagically learns to filter out not
needed information from an input by using convolution kernel.
Pooling layers compute the max or average value of a particular feature over a
region of the input data (downsizing of input images). Also helps to detect objects
in some unusual places and reduces memory size.
33. Deep Learning - Basics
Recurrent Neural Nets (RNN)
general computers which can learn algorithms to map input sequences to
output sequences
RNNs are general computers which can learn algorithms to map input
sequences to output sequences (flexible-sized vectors). The output
vector’s contents are influenced by the entire history of inputs.
general computers which can learn algorithms to map input sequences to
output sequences
State-of-the-art results in time series prediction, adaptive robotics,
handwriting recognition, image classification, speech recognition,
stock market prediction, and other sequence learning problems.
Everything can be processed sequentially.
34. Deep Learning - Basics
Long Short-Term Memory RNN (LSTM)
A Long Short-Term Memory (LSTM) network is a
particular type of recurrent network that works
slightly better in practice, owing to its more
powerful update equation and some appealing
back propagation dynamics.
general computers which can learn algorithms to map input sequences to
output sequences
The LSTM units give the network memory cells with read, write
and reset operations. During training, the network can learn when
it should remember data and when it should throw it away.
general computers which can learn algorithms to map input sequences to
output sequences
Well-suited to learn from experience to classify, process
and predict time series when there are very long time lags of
unknown size between important events.
35. Deep Learning - Basics
Recurrent Neural Nets (RNN) – Generating Text
To train the RNN, insert characters sequentially and
predict the probabilities of the next letter.
Backpropagate error and update RNN’s weights to
increase the confidence of the correct letter (green)
and decrease the confidence of all other letters (red).
Trained on structured Wikipedia markdown. Network learns to spell English words
completely from scratch and copy general syntactic structures.
36. Deep Learning - Basics
Recurrent Neural Nets (RNN) – Generating Text
To generate text, we feed a character into the trained RNN and get a distribution
over what characters are likely to come next (red = likely). We sample from this
distribution, and feed it right back in to get the next letter.
This highlighted neuron gets very excited (green = excited, blue = not excited) when
the RNN is inside the [[ ]] markdown environment and turns off outside of it.
The RNN is likely using this neuron to remember if it is inside a URL or not.
37. Deep Learning - Basics
Image Captioning – Combining CNN and RNN
Neural Image Caption
Generator generates fitting
natural-language captions
only based on the pixels by
combining a vision CNN and
a language-generating RNN.
A man flying through the air
while riding a skateboard
Two pizzas sitting on top of
a stove top oven
A close up of a child holding
a stuffed animal
38. Deep Learning - Basics
Natural Language Processing – Embeddings
Embeddings are used to turn textual data (words, sentences, paragraphs) into high-
dimensional vector representations and group them together with semantically
similar data in a vectorspace. Thereby, computer can detect similarities
mathematically.
39. Deep Learning - Basics
Natural Language Processing – Word2Vec
Word2Vec is an unsupervised learning algorithm for obtaining vector
representations for words. These vectors were trained for a specific domain on
a very large textual data set. GloVe is a better performing alternative.
It detects similarities mathematically by grouping the vectors of similar words together.
All it needs is words co-occurance in the given corpus.
40. Deep Learning - Basics
Natural Language Processing – Word2Vec
Woman – Man ≈ Aunt - Uncle
King - Male + Female ≈ Queen
Human - Animal ≈ Ethics
41. Deep Learning - Basics
Natural Language Processing – Thought Vectors
Thought vectors is a way of embedding thoughts in vector space. Their
features will represent how each thought relates to other thoughts.
By reading every document on the web, computers might be able to
reason like humans do by mimicking the thoughts expressed in content.
A neural machine translation is trained on bilingual text
using a encoder and decoder RNN. For translation, the input
sentence is transformed into a thought vector. This vector is
used to reconstruct the given thought in another language.
42. Deep Learning - Basics
DeepMind Deep Q-Learning
Deep Q-Learning (DQN) is a model-free approach to reinforcement learning
using deep networks in environments with discrete action choices
43. Deep Learning - Basics
DeepMind Deep Q-Learning
Outperforms humans in over 30 Atari games just by receiving the pixels on the
screen with the goal to maximize the score (Reinforcement Learning)
44. Policy distillation: Extracts the learned state (policy) of a
reinforcement learning agent (teacher) and trains a new
network (student) that performs at the expert level while
being dramatically smaller and more efficient.
Single-task policy distillation Multi-task policy distillation
Deep Learning - Basics
DeepMind Deep Q-Learning
45. Deep Learning - Basics
Usage Requirements
Large data set with good quality (input-output mappings)
Measurable and describable goals (define the cost)
Enough computing power (AWS GPU Instance)
Excels in tasks where the basic unit (pixel, word) has very little meaning
in itself, but the combination of such units has a useful meaning
47. Deep Learning - Tools
Computing is affordable
AWS EC2 GPU Spot Instance: g2.2xlarge - $0.0782 per Hour
The DIGITS DevBox combines the
world’s best hardware (4 GPUs),
software, and systems engineering
for deep learning in a powerful
solution that can fit under your
desk. Cost: $15k
48. Outlook
NVIDIA Pascal
NVIDIA’s Pascal GPU architecture will accelerate
deep learning applications up to 10X beyond the
speed of its current-generation Maxwell processors.
49. Outlook
Artificial Quantum Intelligence
Quantum Artificial Intelligence Lab is a joint initiative of NASA and Google to
study how quantum computing might advance machine learning. This type of
computing may provide the most creative and parallelized problem-solving
process under the known laws of physics.
Quantum computers handle what are called quantum bits
or qubits that can readily have a value of one or zero or
anything in between.
Quantum computing represents a paradigm shift, a radical
change in the way we do computing and at a scale that has
unimaginable power – Eric Ladizinsky (Co-founder D-Wave)
50. Outlook
Neuromorphic Chips
IBM TrueNorth is a brain-inspired computer chip that implements
networks of integrate-and-fire spiking artificial neurons and uses
only a tiny 70 mw of power –orders of magnitude less energy
than traditional chips. The system is designed to be able to run
deep-learning algorithms.
1 million
Programmable
Neurons
256 million
Programmable
Synapses
4096
Neurosynaptic
Cores
51. Significant advances in deep reinforcement and unsupervised
learning
Bigger and more complex architectures based on various
interchangeable modules/techniques
Deeper models that can learn from much fewer training cases
Harder problems such as video understanding and natural language
processing will be successfully tackled by deep learning algorithms
Outlook
Deep Learning
52. Takeaways
Machines that learn to represent the world from experience.
Deep Learning is no magic! Just statistics in a black box, but
exceptional effective at learning patterns.
We haven’t figured out creativity and human-empathy.
Transitioning from research to consumer products. Will make the
tools you use every day work better, faster and smarter.