The document provides an introduction to artificial neural networks (ANN). It defines ANNs as systems inspired by biological neural networks that consist of interconnected processing units called neurons. The document outlines the key components of ANNs, including the artificial neuron model, different network architectures, learning strategies, and activation functions. It then discusses applications of ANNs such as pattern recognition, classification, data mapping, and medical diagnosis. In closing, the document notes advantages of ANNs like their ability to model non-linear relationships and adapt to new information.
This document provides an overview of neural networks and fuzzy systems. It outlines a course on the topic, which is divided into two parts: neural networks and fuzzy systems. For neural networks, it covers fundamental concepts of artificial neural networks including single and multi-layer feedforward networks, feedback networks, and unsupervised learning. It also discusses the biological neuron, typical neural network architectures, learning techniques such as backpropagation, and applications of neural networks. Popular activation functions like sigmoid, tanh, and ReLU are also explained.
1. The document describes an introductory course on neural networks. It includes information on topics covered, textbooks, assignments, and report topics.
2. The main topics covered are comprehensive introduction, learning algorithms, and types of neural networks. Report topics include the McCulloch-Pitts model, applications of neural networks, and various learning algorithms.
3. The document also provides background information on biological neural networks and the basic components and functioning of artificial neural networks at a high level.
The document provides an introduction to artificial neural networks (ANNs). It discusses that ANNs are inspired by biological neural systems and composed of interconnected computing units called neurons that can learn from examples like the human brain. There are two main reasons for building ANNs: to solve problems requiring parallel processing like character recognition, and to better understand natural information processing by simulating brain functions. ANNs can be used to model how biological systems like the human brain work in various cognitive tasks and sensory processes.
اسلایدهای درس شبکه عصبی و یادگیری عمیق که در دانشگاه شیراز توسط استاد اقبال منصوری تدریس می شود.
Neural network and deep learning course slide taught by Professor Iqbal Mansouri at Shiraz University.
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.
- Artificial neural networks are computational models inspired by the human brain that are composed of interconnected nodes (neurons) that can learn from examples.
- Knowledge is acquired by adjusting the synaptic connections between neurons based on a learning process. These connections store the knowledge gained during learning.
- There are different network architectures including single-layer feedforward networks, multi-layer feedforward networks, and recurrent networks. Activation functions determine whether a neuron is active.
- Applications of ANNs include pattern recognition, function approximation, and associative memory. Common current models are deep learning architectures and multilayer perceptrons.
This document provides an overview of neural networks and fuzzy systems. It outlines a course on the topic, which is divided into two parts: neural networks and fuzzy systems. For neural networks, it covers fundamental concepts of artificial neural networks including single and multi-layer feedforward networks, feedback networks, and unsupervised learning. It also discusses the biological neuron, typical neural network architectures, learning techniques such as backpropagation, and applications of neural networks. Popular activation functions like sigmoid, tanh, and ReLU are also explained.
1. The document describes an introductory course on neural networks. It includes information on topics covered, textbooks, assignments, and report topics.
2. The main topics covered are comprehensive introduction, learning algorithms, and types of neural networks. Report topics include the McCulloch-Pitts model, applications of neural networks, and various learning algorithms.
3. The document also provides background information on biological neural networks and the basic components and functioning of artificial neural networks at a high level.
The document provides an introduction to artificial neural networks (ANNs). It discusses that ANNs are inspired by biological neural systems and composed of interconnected computing units called neurons that can learn from examples like the human brain. There are two main reasons for building ANNs: to solve problems requiring parallel processing like character recognition, and to better understand natural information processing by simulating brain functions. ANNs can be used to model how biological systems like the human brain work in various cognitive tasks and sensory processes.
اسلایدهای درس شبکه عصبی و یادگیری عمیق که در دانشگاه شیراز توسط استاد اقبال منصوری تدریس می شود.
Neural network and deep learning course slide taught by Professor Iqbal Mansouri at Shiraz University.
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.
- Artificial neural networks are computational models inspired by the human brain that are composed of interconnected nodes (neurons) that can learn from examples.
- Knowledge is acquired by adjusting the synaptic connections between neurons based on a learning process. These connections store the knowledge gained during learning.
- There are different network architectures including single-layer feedforward networks, multi-layer feedforward networks, and recurrent networks. Activation functions determine whether a neuron is active.
- Applications of ANNs include pattern recognition, function approximation, and associative memory. Common current models are deep learning architectures and multilayer perceptrons.
This document discusses artificial neural networks (ANNs). It begins with an introduction and overview of biological neural networks. It then discusses ANNs, their relationship to biological neural networks, and their ability to perform tasks like classification. The document compares von Neumann computers to biological neural systems. It discusses learning in ANNs and different learning paradigms like supervised, unsupervised, and reinforcement learning. It also covers network architectures, common learning algorithms, and backpropagation for training multilayer feedforward neural networks.
The document provides an overview of artificial neural networks and biological neural networks. It discusses the components and functions of the human nervous system including the central nervous system made up of the brain and spinal cord, as well as the peripheral nervous system. The four main parts of the brain - cerebrum, cerebellum, diencephalon, and brainstem - are described along with their roles in processing sensory information and controlling bodily functions. A brief history of artificial neural networks is also presented.
Artificial neural networks (ANNs) are computational models inspired by biological neural networks in the human brain. ANNs contain artificial neurons that are interconnected in layers and transmit signals to one another. The connections between neurons are associated with weights that are adjusted during training to produce the desired output. ANNs can learn complex patterns and relationships through a process of trial and error. They are widely used for tasks like pattern recognition, classification, prediction, and data clustering.
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
Neural networks of artificial intelligencealldesign
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs, performs calculations, and outputs a value. ANNs can be trained to learn patterns from data through examples to perform tasks like classification, prediction, clustering, and association. Common ANN architectures include multilayer perceptrons, convolutional neural networks, and recurrent neural networks.
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
Introduction to Artificial Neural NetworkAmeer H Ali
This document provides an introduction to artificial neural networks (ANNs). It outlines the key components of biological neurons, including dendrites, soma, and axon. ANNs are then introduced as computational networks designed to simulate biological neural behavior. The typical form of an artificial neuron is described, including weighted inputs, summation, and an activation function. Differences between biological and artificial neural networks are noted, such as biological networks having broader capabilities. Applications of ANNs mentioned include signal processing, pattern recognition, and image processing. A brief history of the field is also presented.
This document provides an overview of artificial neural networks. It describes the biological neuron model that inspired artificial networks, with dendrites receiving inputs, the soma processing them, the axon transmitting outputs, and synapses connecting neurons. An artificial neuron model is presented that uses weighted inputs, a summation function, and an activation function to generate outputs. The document discusses unsupervised and supervised learning methods, and lists applications such as character recognition, stock prediction, and medicine. Advantages include human-like thinking and handling noisy data, while disadvantages include the need for training and high processing times.
This document provides an overview of artificial neural networks. It begins with definitions of artificial neural networks and how they are analogous to biological neural networks. It then discusses the basic structure of artificial neural networks, including different types of networks like feedforward, recurrent, and convolutional networks. Key concepts in artificial neural networks like neurons, weights, forward/backward propagation, and overfitting/underfitting are also explained. The document concludes with limitations of neural networks and references.
This document provides an overview of artificial neural networks. It discusses the biological neuron model that inspired artificial neural networks. The key components of an artificial neuron are inputs, weights, summation, and an activation function. Neural networks have an interconnected architecture with layers of nodes. Learning involves modifying the weights through algorithms like backpropagation to minimize error. Neural networks can perform supervised or unsupervised learning. Their advantages include handling complex nonlinear problems, learning from data, and adapting to new situations.
Artificial Neural Networks ppt.pptx for final sem cseNaveenBhajantri1
This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.
This document provides an overview of artificial neural networks (ANNs). It discusses the history of ANNs beginning in the 1940s and important developments like the perceptron in 1957 and backpropagation algorithms in the 1970s and 1980s. The document defines ANNs as consisting of interconnected processing units (neurons) that communicate by sending signals to each other via weighted connections, and learns from experience through training. It also compares ANNs to the human brain in using a highly parallel and distributed approach to problem solving.
Neural networks, also referred to as Artificial Neural Networks (ANNs), are computational models that draw inspiration from the structure and operations of the human brain. They comprise interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge.
Neural networks, also referred to as Artificial Neural Networks (ANNs), are computational models that draw inspiration from the structure and operations of the human brain. They comprise interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge.
Neural networks, also referred to as Artificial Neural Networks (ANNs), are computational models that draw inspiration from the structure and operations of the human brain. They comprise interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge.
The document discusses fundamentals of neural networks and artificial intelligence. It provides an overview of topics covered in lectures 37 and 38, including the biological neuron model, artificial neuron model, neural network architectures, learning methods in neural networks, single-layer neural network systems, and applications of neural networks. It also includes details on the McCulloch-Pitts neuron model and the basic elements of an artificial neuron, such as weights, thresholds, and activation functions.
This document provides an overview of neural networks, including their history, components, connection types, learning methods, applications, and comparison to conventional computers. It discusses how biological neurons inspired the development of artificial neurons and neural networks. The key components of biological and artificial neurons are described. Connection types in neural networks include static feedforward and dynamic feedbackward connections. Learning methods include supervised, unsupervised, and reinforcement learning. Applications span mobile computing, forecasting, character recognition, and more. Neural networks learn by example rather than requiring explicitly programmed algorithms.
Neural networks are mathematical models inspired by biological neural networks. They are useful for pattern recognition and data classification through a learning process of adjusting synaptic connections between neurons. A neural network maps input nodes to output nodes through an arbitrary number of hidden nodes. It is trained by presenting examples to adjust weights using methods like backpropagation to minimize error between actual and predicted outputs. Neural networks have advantages like noise tolerance and not requiring assumptions about data distributions. They have applications in finance, marketing, and other fields, though designing optimal network topology can be challenging.
Artificial neural networks (ANNs) are computational models inspired by the human brain that are used for predictive analytics and nonlinear statistical modeling. ANNs can learn complex patterns and relationships from large datasets through a process of training, and then make predictions on new data. The three most common types of ANN architectures are multilayer perceptrons, radial basis function networks, and self-organizing maps. ANNs have been successfully applied across many domains, including finance, medicine, engineering, and biology, to solve problems involving classification, prediction, and nonlinear pattern recognition.
This document discusses artificial neural networks (ANNs). It begins with an introduction and overview of biological neural networks. It then discusses ANNs, their relationship to biological neural networks, and their ability to perform tasks like classification. The document compares von Neumann computers to biological neural systems. It discusses learning in ANNs and different learning paradigms like supervised, unsupervised, and reinforcement learning. It also covers network architectures, common learning algorithms, and backpropagation for training multilayer feedforward neural networks.
The document provides an overview of artificial neural networks and biological neural networks. It discusses the components and functions of the human nervous system including the central nervous system made up of the brain and spinal cord, as well as the peripheral nervous system. The four main parts of the brain - cerebrum, cerebellum, diencephalon, and brainstem - are described along with their roles in processing sensory information and controlling bodily functions. A brief history of artificial neural networks is also presented.
Artificial neural networks (ANNs) are computational models inspired by biological neural networks in the human brain. ANNs contain artificial neurons that are interconnected in layers and transmit signals to one another. The connections between neurons are associated with weights that are adjusted during training to produce the desired output. ANNs can learn complex patterns and relationships through a process of trial and error. They are widely used for tasks like pattern recognition, classification, prediction, and data clustering.
The document discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
Neural networks of artificial intelligencealldesign
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs, performs calculations, and outputs a value. ANNs can be trained to learn patterns from data through examples to perform tasks like classification, prediction, clustering, and association. Common ANN architectures include multilayer perceptrons, convolutional neural networks, and recurrent neural networks.
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
Introduction to Artificial Neural NetworkAmeer H Ali
This document provides an introduction to artificial neural networks (ANNs). It outlines the key components of biological neurons, including dendrites, soma, and axon. ANNs are then introduced as computational networks designed to simulate biological neural behavior. The typical form of an artificial neuron is described, including weighted inputs, summation, and an activation function. Differences between biological and artificial neural networks are noted, such as biological networks having broader capabilities. Applications of ANNs mentioned include signal processing, pattern recognition, and image processing. A brief history of the field is also presented.
This document provides an overview of artificial neural networks. It describes the biological neuron model that inspired artificial networks, with dendrites receiving inputs, the soma processing them, the axon transmitting outputs, and synapses connecting neurons. An artificial neuron model is presented that uses weighted inputs, a summation function, and an activation function to generate outputs. The document discusses unsupervised and supervised learning methods, and lists applications such as character recognition, stock prediction, and medicine. Advantages include human-like thinking and handling noisy data, while disadvantages include the need for training and high processing times.
This document provides an overview of artificial neural networks. It begins with definitions of artificial neural networks and how they are analogous to biological neural networks. It then discusses the basic structure of artificial neural networks, including different types of networks like feedforward, recurrent, and convolutional networks. Key concepts in artificial neural networks like neurons, weights, forward/backward propagation, and overfitting/underfitting are also explained. The document concludes with limitations of neural networks and references.
This document provides an overview of artificial neural networks. It discusses the biological neuron model that inspired artificial neural networks. The key components of an artificial neuron are inputs, weights, summation, and an activation function. Neural networks have an interconnected architecture with layers of nodes. Learning involves modifying the weights through algorithms like backpropagation to minimize error. Neural networks can perform supervised or unsupervised learning. Their advantages include handling complex nonlinear problems, learning from data, and adapting to new situations.
Artificial Neural Networks ppt.pptx for final sem cseNaveenBhajantri1
This document provides an overview of artificial neural networks. It discusses the biological inspiration from neurons in the brain and how artificial neural networks mimic this structure. The key components of artificial neurons and various network architectures are described, including fully connected, layered, feedforward, and modular networks. Supervised and unsupervised learning approaches are covered, with backpropagation highlighted as a commonly used supervised algorithm. Applications of neural networks are mentioned in areas like medicine, business, marketing and credit evaluation. Advantages include the ability to handle complex nonlinear problems and noisy data.
This document provides an overview of artificial neural networks (ANNs). It discusses the history of ANNs beginning in the 1940s and important developments like the perceptron in 1957 and backpropagation algorithms in the 1970s and 1980s. The document defines ANNs as consisting of interconnected processing units (neurons) that communicate by sending signals to each other via weighted connections, and learns from experience through training. It also compares ANNs to the human brain in using a highly parallel and distributed approach to problem solving.
Neural networks, also referred to as Artificial Neural Networks (ANNs), are computational models that draw inspiration from the structure and operations of the human brain. They comprise interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge.
Neural networks, also referred to as Artificial Neural Networks (ANNs), are computational models that draw inspiration from the structure and operations of the human brain. They comprise interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge.
Neural networks, also referred to as Artificial Neural Networks (ANNs), are computational models that draw inspiration from the structure and operations of the human brain. They comprise interconnected nodes, or artificial neurons, organized in layers. Neural networks are designed to process and examine complex data, recognize patterns, and make predictions or decisions based on their learned knowledge.
The document discusses fundamentals of neural networks and artificial intelligence. It provides an overview of topics covered in lectures 37 and 38, including the biological neuron model, artificial neuron model, neural network architectures, learning methods in neural networks, single-layer neural network systems, and applications of neural networks. It also includes details on the McCulloch-Pitts neuron model and the basic elements of an artificial neuron, such as weights, thresholds, and activation functions.
This document provides an overview of neural networks, including their history, components, connection types, learning methods, applications, and comparison to conventional computers. It discusses how biological neurons inspired the development of artificial neurons and neural networks. The key components of biological and artificial neurons are described. Connection types in neural networks include static feedforward and dynamic feedbackward connections. Learning methods include supervised, unsupervised, and reinforcement learning. Applications span mobile computing, forecasting, character recognition, and more. Neural networks learn by example rather than requiring explicitly programmed algorithms.
Neural networks are mathematical models inspired by biological neural networks. They are useful for pattern recognition and data classification through a learning process of adjusting synaptic connections between neurons. A neural network maps input nodes to output nodes through an arbitrary number of hidden nodes. It is trained by presenting examples to adjust weights using methods like backpropagation to minimize error between actual and predicted outputs. Neural networks have advantages like noise tolerance and not requiring assumptions about data distributions. They have applications in finance, marketing, and other fields, though designing optimal network topology can be challenging.
Artificial neural networks (ANNs) are computational models inspired by the human brain that are used for predictive analytics and nonlinear statistical modeling. ANNs can learn complex patterns and relationships from large datasets through a process of training, and then make predictions on new data. The three most common types of ANN architectures are multilayer perceptrons, radial basis function networks, and self-organizing maps. ANNs have been successfully applied across many domains, including finance, medicine, engineering, and biology, to solve problems involving classification, prediction, and nonlinear pattern recognition.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
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Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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For more information about PECB:
Website: https://pecb.com/
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Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
2. Definition, why and how are neural
networks being used in solving problems
Human biological neuron
Artificial Neuron
Comparison of ANN vs conventional AI
methods
Outline
Applications of ANN
3. The idea of ANNs..?
NNs learn relationship between cause and effect or
organize large volumes of data into orderly and
informative patterns.
frog
lion
bird
What is that?
It’s a frog
4. 4
Neural networks to the rescue…
• Neural network: information processing
paradigm inspired by biological nervous
systems, such as our brain
• Structure: large number of highly interconnected
processing elements (neurons) working together
• Like people, they learn from experience (by
example)
5. 5
Definition of ANN
“Data processing system consisting of a
large number of simple, highly
interconnected processing elements
(artificial neurons) in an architecture inspired
by the structure of the cerebral cortex of the
brain”
(Tsoukalas & Uhrig, 1997).
8. Biological Neural Networks
A biological neuron has
three types of main
components; dendrites,
soma (or cell body) and
axon.
Dendrites receives
signals from other
neurons.
The soma, sums the incoming signals. When
sufficient input is received, the cell fires; that is it
transmit a signal over its axon to other cells.
9. Artificial Neurons
ANN is an information processing system that has
certain performance characteristics in common
with biological nets.
Several key features of the processing elements of
ANN are suggested by the properties of biological
neurons:
1. The processing element receives many signals.
2. Signals may be modified by a weight at the receiving
synapse.
3. The processing element sums the weighted inputs.
4. Under appropriate circumstances (sufficient input), the
neuron transmits a single output.
5. The output from a particular neuron may go to many other
neurons.
10. 10
• From experience:
examples / training
data
• Strength of connection
between the neurons
is stored as a weight-
value for the specific
connection.
• Learning the solution
to a problem =
changing the
connection weights
A physical neuron
An artificial neuron
Artificial Neurons
11. Artificial Neurons
ANNs have been developed as generalizations of
mathematical models of neural biology, based on
the assumptions that:
1. Information processing occurs at many simple elements
called neurons.
2. Signals are passed between neurons over connection links.
3. Each connection link has an associated weight, which, in
typical neural net, multiplies the signal transmitted.
4. Each neuron applies an activation function to its net input
to determine its output signal.
12. 12
Four basic components of a human biological
neuron
The components of a basic artificial neuron
Artificial Neuron
13. 13
Model Of A Neuron
f()
Y
Wa
Wb
Wc
Connection
weights
Summing
function
computation
X1
X3
X2
Input units
(dendrite) (synapse) (axon)
(soma)
14. 14
• A neural net consists of a large number of
simple processing elements called neurons,
units, cells or nodes.
• Each neuron is connected to other neurons by
means of directed communication links, each
with associated weight.
• The weight represent information being used by
the net to solve a problem.
15. 15
• Each neuron has an internal state, called
its activation or activity level, which is a
function of the inputs it has received.
Typically, a neuron sends its activation as
a signal to several other neurons.
• It is important to note that a neuron can
send only one signal at a time, although
that signal is broadcast to several other
neurons.
16. 16
• Neural networks are configured for a specific
application, such as pattern recognition or
data classification, through a learning
process
• In a biological system, learning involves
adjustments to the synaptic connections
between neurons
same for artificial neural networks (ANNs)
17. 17
x2
w1
w2
x1
Dendrite
Axon
yin = x1w1 + x2w2
Nukleus
Activation Function:
(y-in) = 1 if y-in >=
and (y-in) = 0
y
-A neuron receives input, determines the strength or the weight of the input, calculates the total
weighted input, and compares the total weighted with a value (threshold)
-The value is in the range of 0 and 1
- If the total weighted input greater than or equal the threshold value, the neuron will produce the
output, and if the total weighted input less than the threshold value, no output will be produced
Synapse
Artificial Neural Network
19. 19
• 1977 Brain State in a Box (Anderson)
• 1982 Hopfield net, constraint satisfaction
• 1985 ART (Carpenter, Grossfield)
• 1986 Backpropagation (Rumelhart, Hinton,
McClelland)
• 1988 Neocognitron, character recognition
(Fukushima)
20. 20
Characterization
• Architecture
– a pattern of connections between neurons
• Single Layer Feedforward
• Multilayer Feedforward
• Recurrent
• Strategy / Learning Algorithm
– a method of determining the connection weights
• Supervised
• Unsupervised
• Reinforcement
• Activation Function
– Function to compute output signal from input signal
21. 21
Single Layer Feedforward NN
x2
w11
w12
x1
w21
w22
ym
yn
Input layer
output layer
Contoh: ADALINE, AM, Hopfield, LVQ, Perceptron, SOFM
24. 24
Strategy / Learning Algorithm
• Learning is performed by presenting pattern with target
• During learning, produced output is compared with the desired output
– The difference between both output is used to modify learning
weights according to the learning algorithm
• Recognizing hand-written digits, pattern recognition and etc.
• Neural Network models: perceptron, feed-forward, radial basis function,
support vector machine.
Supervised Learning
25. 25
• Targets are not provided
• Appropriate for clustering task
– Find similar groups of documents in the web, content
addressable memory, clustering.
• Neural Network models: Kohonen, self organizing maps,
Hopfield networks.
Unsupervised Learning
26. 26
• Target is provided, but the desired output is absent.
• The net is only provided with guidance to determine the
produced output is correct or vise versa.
• Weights are modified in the units that have errors
Reinforcement Learning
29. 29
x2
w1= 0.5
w2 = 0.3
x1
yin = x1w1 + x2w2
y
Activation Function:
Binary Step Function
= 0.5,
(y-in) = 1 if y-in >=
dan (y-in) = 0
30. 30
Where can neural network systems help…
• when we can't formulate an algorithmic
solution.
• when we can get lots of examples of the
behavior we require.
‘learning from experience’
• when we need to pick out the structure
from existing data.
31. 31
Who is interested?...
• Electrical Engineers – signal processing,
control theory
• Computer Engineers – robotics
• Computer Scientists – artificial
intelligence, pattern recognition
• Mathematicians – modelling tool when
explicit relationships are unknown
32. 32
Problem Domains
• Storing and recalling patterns
• Classifying patterns
• Mapping inputs onto outputs
• Grouping similar patterns
• Finding solutions to constrained
optimization problems
36. 36
• Signal processing
• Pattern recognition, e.g. handwritten
characters or face identification.
• Diagnosis or mapping symptoms to a
medical case.
• Speech recognition
• Human Emotion Detection
• Educational Loan Forecasting
Applications of ANNs
40. 40
NON-LINEARITY
It can model non-linear systems
INPUT-OUTPUT MAPPING
It can derive a relationship between a set of input & output
responses
ADAPTIVITY
The ability to learn allows the network to adapt to changes in
the surrounding environment
EVIDENTIAL RESPONSE
It can provide a confidence level to a given solution
Advantages Of NN
41. 41
CONTEXTUAL INFORMATION
Knowledge is presented by the structure of the network.
Every neuron in the network is potentially affected by the
global activity of all other neurons in the network.
Consequently, contextual information is dealt with naturally in
the network.
FAULT TOLERANCE
Distributed nature of the NN gives it fault tolerant capabilities
NEUROBIOLOGY ANALOGY
Models the architecture of the brain
Advantages Of NN