The document discusses several types of artificial neural network architectures:
- The Perceptron network classifies inputs into categories by adjusting weights between input and output units.
- The Adaline network receives multiple inputs and one bias input, with weights that are positive or negative. It compares actual and predicted outputs.
- The Madaline network contains input, Adaline, and output layers. It is used in communication systems for equalization and noise cancellation.
- The Backpropagation network is a multilayer feedforward network that calculates outputs from inputs and uses backward signals in learning.
- The Autoassociative memory network trains inputs to match outputs using weighted interconnections between identical input and output layers.
- The Maxnet network
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI.
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
Contains description of CPN.
CP algorithm consists of a input, hidden and output layer.
In this case the hidden layer is called the Kohonen layer & the output layer is called the Grossberg layer.
In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI.
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
Contains description of CPN.
CP algorithm consists of a input, hidden and output layer.
In this case the hidden layer is called the Kohonen layer & the output layer is called the Grossberg layer.
Artificial Intelligence: Artificial Neural NetworksThe Integral Worm
This presentation covers artificial neural networks for artificial intelligence. Topics covered are as follows: artificial neural networks, basic representation, hidden units, exclusive OR problem, backpropagation, advantages of artificial neural networks, properties of artificial neural networks, and disadvantages of artificial neural networks.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Abstract: This PDSG workshop introduces basic concepts of the grandfather of neural networks - the Perceptron. Concepts covered are history, algorithm and limitations.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Machine Learning - Introduction to Neural NetworksAndrew Ferlitsch
Abstract: This PDSG workshop introduces basic concepts of neural networks. Concepts covered are Neurons, Binary vs. Categorical vs. Real Value output, activation functions, fully connected networks, deep neural networks, specialized learners, cost function and feed forward.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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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.
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
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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.
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.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. Perceptron Network
• Weights between
input & output
units are
adjusted.
• Weights between
sensory
associator units
are fixed.
• Goal of
Perceptron net is
to classify the
input pattern as a
member on not
a member to a
particular class.
X1
Xi
1
Xn
Y
X0
X1
Xi
Xn
y
b
W1
W2
Wn
3. Adaline Network
• Receives input from
several units and one
unit called bias.
• Inputs are +1 or -1,
weights have sign +
or -
• Net input calculated
is applied to
quantizer function to
restore output to +1
or -1
• Compares actual
4. Madaline Network
• Contains “n” units of
input layer,”m” units of
adaline layers, “1” unit
of Madaline Layer.
• Each neuron in the
Adaline and madaline
layer have a bias of
excitation 1.
• Adaline layer is present
between input and
output Madaline Layer.
• Used in
Communication
Systems , equilizers and
noise cancellation
devices.
5. Back Propagation Network
• A multilayer Feed
forward network
consisting of Input,
hidden and output
layers.
• Hidden and output
layers have biases
whose activation is 1.
• Signals are reversed
in learning phase.
• Inputs sent to BPN
and outputs
obtained could be
6. Auto Associative Memory Network
• Training input and
target output vectors
are same.
• Input layers consist of n
input units & output
layer consist of n
output units.
• Input and output units
are connected
through weighted
interconnections.
• Input and output
vectors are perfectly
correlated with each
other component by
7. Maxnet
• Symmetrical weights
are present over the
weighted
interconnections.
• Weights between
neurons are inhibitory
and fixed.
• The maxnet with this
structure can be
used as a subnet to
select a particular
node whose net
input is the largest.
X1 Xm
Xi Xj
1 1
1
−𝜀
−𝜀
−𝜀
−𝜀
−𝜀
−𝜀
1
8. Mexican Hat Net
• Neurons are arranged
in a linear order such
that positive
connections exist
between Xi and
neighborhood units &
negative between Xi
and far away units.
• Positive region is
Cooperation and
negative region is
Competition.
• Size of these regions
depend on the
magnitude that exist
between positive and
X i X
i+1
X
i+2
X
i+3
X
i-1
X
i-2
X
i-3
W3
W3
W2 W2
W1 W1
𝛿𝑖
W0
Editor's Notes
Learning signal is the difference between the
desired and actual response of a neuron.
The perceptron learning rule is
Consider a finite “n” number of input training vectors
Associated target (desired) values x(n) and t(n) where n is
from 1 to N
Target is either +1 or -1
The output “y” is obtained on the basis of the net input
calculated and activation function being applied over the
net input