The document provides an overview of neural networks and the backpropagation algorithm for training neural networks. It defines the basic components of a neural network including neurons, layers, weights, and biases. It then explains how a multilayer feedforward network is structured and how backpropagation works by propagating errors backward from the output to earlier layers to update weights and biases to minimize classification errors on training data. The process involves feeding inputs forward, calculating outputs at each layer, computing errors at the output layer, and propagating errors back to update the weights.
10 Backpropagation Algorithm for Neural Networks (1).pptxSaifKhan703888
This document discusses neural network classification using backpropagation. It begins by introducing backpropagation as a neural network learning algorithm. It then explains how a multi-layer neural network works, involving propagating inputs forward and backpropagating errors to update weights. The document provides a detailed example to illustrate backpropagation. It also discusses defining network topology, improving efficiency and interpretability, and some strengths and weaknesses of neural network classification.
The document describes multilayer neural networks and their use for classification problems. It discusses how neural networks can handle continuous-valued inputs and outputs unlike decision trees. Neural networks are inherently parallel and can be sped up through parallelization techniques. The document then provides details on the basic components of neural networks, including neurons, weights, biases, and activation functions. It also describes common network architectures like feedforward networks and discusses backpropagation for training networks.
The slide covers the basic concepts and designs of artificial neural networks. It explains and justifies the use of McCulloh Pitts Model, Adaline network, Perceptron algorithm, Backpropagation algorithm, Hopfield network and Kohonen network; along with its practical applications.
The document discusses artificial neural networks and classification using backpropagation, describing neural networks as sets of connected input and output units where each connection has an associated weight. It explains backpropagation as a neural network learning algorithm that trains networks by adjusting weights to correctly predict the class label of input data, and how multi-layer feed-forward neural networks can be used for classification by propagating inputs through hidden layers to generate outputs.
The document describes a multilayer neural network presentation. It discusses key concepts of neural networks including their architecture, types of neural networks, and backpropagation. The key points are:
1) Neural networks are composed of interconnected processing units (neurons) that can learn relationships in data through training. They are inspired by biological neural systems.
2) Common network architectures include multilayer perceptrons and recurrent networks. Backpropagation is commonly used to train multilayer feedforward networks by propagating errors backwards.
3) Neural networks have advantages like the ability to model complex nonlinear relationships, adapt to new data, and extract patterns from imperfect data. They are well-suited for problems like classification.
Artificial Neural Networks Lect2: Neurobiology & Architectures of ANNSMohammed Bennamoun
This document discusses the structure and function of biological neurons and artificial neural networks (ANNs). It covers topics such as:
- The basic components of biological neurons including the cell body, dendrites, axon, and synapses.
- Models of artificial neurons including linear and nonlinear activation functions.
- Different types of neural network architectures including feedforward, recurrent, and feedback networks.
- Training algorithms for ANNs including supervised and unsupervised learning methods. Weights are modified to minimize error between network outputs and training targets.
This document discusses backpropagation neural networks. It begins with an introduction to backpropagation and gradient descent optimization. It then describes the architecture of a backpropagation network, including input, hidden, and output layers connected by weights. The training algorithm is explained in detail, including feedforward calculation, backpropagation of error, weight/bias updates, and activation functions. It concludes with discussions of initializing weights randomly or with the Nguyen-Widrow method and a graph showing error reduction over iterations.
The document discusses different types of machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. It then provides details on artificial neural networks, describing them as consisting of simple processing units that communicate through weighted connections, similar to neurons in the human brain. The document outlines key aspects of artificial neural networks like processing units, connections between units, propagation rules, and learning methods.
10 Backpropagation Algorithm for Neural Networks (1).pptxSaifKhan703888
This document discusses neural network classification using backpropagation. It begins by introducing backpropagation as a neural network learning algorithm. It then explains how a multi-layer neural network works, involving propagating inputs forward and backpropagating errors to update weights. The document provides a detailed example to illustrate backpropagation. It also discusses defining network topology, improving efficiency and interpretability, and some strengths and weaknesses of neural network classification.
The document describes multilayer neural networks and their use for classification problems. It discusses how neural networks can handle continuous-valued inputs and outputs unlike decision trees. Neural networks are inherently parallel and can be sped up through parallelization techniques. The document then provides details on the basic components of neural networks, including neurons, weights, biases, and activation functions. It also describes common network architectures like feedforward networks and discusses backpropagation for training networks.
The slide covers the basic concepts and designs of artificial neural networks. It explains and justifies the use of McCulloh Pitts Model, Adaline network, Perceptron algorithm, Backpropagation algorithm, Hopfield network and Kohonen network; along with its practical applications.
The document discusses artificial neural networks and classification using backpropagation, describing neural networks as sets of connected input and output units where each connection has an associated weight. It explains backpropagation as a neural network learning algorithm that trains networks by adjusting weights to correctly predict the class label of input data, and how multi-layer feed-forward neural networks can be used for classification by propagating inputs through hidden layers to generate outputs.
The document describes a multilayer neural network presentation. It discusses key concepts of neural networks including their architecture, types of neural networks, and backpropagation. The key points are:
1) Neural networks are composed of interconnected processing units (neurons) that can learn relationships in data through training. They are inspired by biological neural systems.
2) Common network architectures include multilayer perceptrons and recurrent networks. Backpropagation is commonly used to train multilayer feedforward networks by propagating errors backwards.
3) Neural networks have advantages like the ability to model complex nonlinear relationships, adapt to new data, and extract patterns from imperfect data. They are well-suited for problems like classification.
Artificial Neural Networks Lect2: Neurobiology & Architectures of ANNSMohammed Bennamoun
This document discusses the structure and function of biological neurons and artificial neural networks (ANNs). It covers topics such as:
- The basic components of biological neurons including the cell body, dendrites, axon, and synapses.
- Models of artificial neurons including linear and nonlinear activation functions.
- Different types of neural network architectures including feedforward, recurrent, and feedback networks.
- Training algorithms for ANNs including supervised and unsupervised learning methods. Weights are modified to minimize error between network outputs and training targets.
This document discusses backpropagation neural networks. It begins with an introduction to backpropagation and gradient descent optimization. It then describes the architecture of a backpropagation network, including input, hidden, and output layers connected by weights. The training algorithm is explained in detail, including feedforward calculation, backpropagation of error, weight/bias updates, and activation functions. It concludes with discussions of initializing weights randomly or with the Nguyen-Widrow method and a graph showing error reduction over iterations.
The document discusses different types of machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. It then provides details on artificial neural networks, describing them as consisting of simple processing units that communicate through weighted connections, similar to neurons in the human brain. The document outlines key aspects of artificial neural networks like processing units, connections between units, propagation rules, and learning methods.
This document discusses neural networks and their applications. It begins with an overview of neurons and the brain, then describes the basic components of neural networks including layers, nodes, weights, and learning algorithms. Examples are given of early neural network designs from the 1940s-1980s and their applications. The document also summarizes backpropagation learning in multi-layer networks and discusses common network architectures like perceptrons, Hopfield networks, and convolutional networks. In closing, it notes the strengths and limitations of neural networks along with domains where they have proven useful, such as recognition, control, prediction, and categorization tasks.
- The document presents a neural network model for recognizing handwritten digits. It uses a dataset of 20x20 pixel grayscale images of digits 0-9.
- The proposed neural network has an input layer of 400 nodes, a hidden layer of 25 nodes, and an output layer of 10 nodes. It is trained using backpropagation to classify images.
- The model achieves an accuracy of over 96.5% on test data after 200 iterations of training, outperforming a logistic regression model which achieved 91.5% accuracy. Future work could involve classifying more complex natural images.
the slides are aimed to give a brief introductory base to Neural Networks and its architectures. it covers logistic regression, shallow neural networks and deep neural networks. the slides were presented in Deep Learning IndabaX Sudan.
Multilayer Backpropagation Neural Networks for Implementation of Logic GatesIJCSES Journal
ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn
from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is
designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained
by using a backpropagation algorithm until the model satisfies the predefined error criteria (e)
which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The
NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For
OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
The document provides an overview of backpropagation, a common algorithm used to train multi-layer neural networks. It discusses:
- How backpropagation works by calculating error terms for output nodes and propagating these errors back through the network to adjust weights.
- The stages of feedforward activation and backpropagation of errors to update weights.
- Options like initial random weights, number of training cycles and hidden nodes.
- An example of using backpropagation to train a network to learn the XOR function over multiple training passes of forward passing and backward error propagation and weight updating.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
This document provides an overview of neural networks and related topics. It begins with an introduction to neural networks and discusses natural neural networks, early artificial neural networks, modeling neurons, and network design. It then covers multi-layer neural networks, perceptron networks, training, and advantages of neural networks. Additional topics include fuzzy logic, genetic algorithms, clustering, and adaptive neuro-fuzzy inference systems (ANFIS).
The document discusses backpropagation, which is a popular neural network learning algorithm. It describes the key components of a neural network including the input, hidden, and output layers. During training, weights are adjusted to minimize error between the network's predictions and actual outputs. Backpropagation works by propagating error backwards from the output layer through hidden layers to update weights and biases using gradient descent. This helps the network learn and improve its ability to accurately predict the class labels of new input samples.
This document discusses neural networks and their learning capabilities. It describes how neural networks are composed of simple interconnected elements that can learn patterns from examples through training. Perceptrons are introduced as single-layer neural networks that can learn linearly separable functions through a simple learning rule. Multi-layer networks are shown to have greater learning capabilities than perceptrons using an algorithm called backpropagation that propagates errors backward through the network to update weights. Applications of neural networks include pattern recognition, control problems, and time series prediction tasks.
The document discusses neural networks and how they address limitations of polynomial hypotheses for classification tasks. It explains that neural networks use layers of simulated neurons to learn hierarchical representations of data. Lower layers of neurons in a neural network learn simple features that are combined in higher layers to learn more complex patterns and classify input data. This architecture allows neural networks to learn appropriate representations for tasks like computer vision without needing to explicitly define high-order polynomial features.
Artificial neural networks are computational models inspired by the human brain. They are composed of interconnected nodes that process information using a technique called machine learning. This report discusses the basic components of neural networks including neurons, layers, and training methods. It also provides examples of using neural networks to learn and implement simple logic functions like AND, OR, NAND, and NOR gates. The code shows how neural networks can be built and trained in MATLAB to recognize patterns in input data and produce the correct output.
Neural networks are computing systems inspired by the human brain that are composed of interconnected nodes similar to neurons. They can recognize complex patterns in raw data through learning algorithms. An artificial neural network consists of layers of nodes - an input layer, one or more hidden layers, and an output layer. Weights are assigned to connections between nodes and are adjusted during training to produce the desired output.
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 and applies an activation function to produce an output. ANNs can learn from examples through a process of adjusting the weights between neurons. Backpropagation is a common learning algorithm that propagates errors backward from the output to adjust weights and minimize errors. While single-layer perceptrons can only model linearly separable problems, multi-layer feedforward neural networks can handle non-linear problems using hidden layers that allow the network to learn complex patterns from data.
The document provides information about artificial neural networks and how they relate to biological neural networks. It discusses:
- The key components of biological neurons and how they transmit signals.
- How artificial neural networks are modeled after biological neural networks, with artificial neurons and weighted connections between them.
- The main types of neural network architectures - feedforward and recurrent networks. Feedforward networks have no feedback loops, while recurrent networks have feedback loops.
- How neural networks learn by adjusting the weights between neurons through various learning rules and algorithms, like Hebbian learning and backpropagation, to minimize error between the actual and desired output.
ANNs have been widely used in various domains for: Pattern recognition Funct...vijaym148
The document discusses artificial neural networks (ANNs), which are computational models inspired by the human brain. ANNs consist of interconnected nodes that mimic neurons in the brain. Knowledge is stored in the synaptic connections between neurons. ANNs can be used for pattern recognition, function approximation, and associative memory. Backpropagation is an important algorithm for training multilayer ANNs by adjusting the synaptic weights based on examples. ANNs have been applied to problems like image classification, speech recognition, and financial prediction.
This document discusses machine learning classification using a single layer feed forward neural network. It begins with definitions of machine learning and the different types of machine learning problems. Supervised learning classification is explained where the goal is to learn from labeled training data to classify new observations. Common classification algorithms and the components of a learning model are described. Finally, the document provides an example of how a single layer perceptron neural network can be used to classify a sample dataset into two classes by learning the optimal weights through an iterative process.
The document discusses neural networks and their ability to perform non-linear classification. It describes how neural networks can learn complex patterns in data through multiple layers of nonlinear transformations. The key algorithms covered are the forward pass to perform inference and the backward pass using backpropagation for learning network weights. Backpropagation efficiently computes gradients through the network to optimize weights with gradient descent. The document provides examples of network architecture, activation functions, loss functions, and the mathematical details of backpropagation for multi-layer neural networks.
The document discusses components and concepts related to artificial neural networks. It describes the basic units (neurons), connections between neurons, propagation and activation functions, common activation functions like sigmoid and tanh, and network topologies including feedforward and recurrent networks. It provides details on how artificial neural networks are designed based on the human brain and how information is processed through the connections and activation of neurons.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
This document discusses neural networks and their applications. It begins with an overview of neurons and the brain, then describes the basic components of neural networks including layers, nodes, weights, and learning algorithms. Examples are given of early neural network designs from the 1940s-1980s and their applications. The document also summarizes backpropagation learning in multi-layer networks and discusses common network architectures like perceptrons, Hopfield networks, and convolutional networks. In closing, it notes the strengths and limitations of neural networks along with domains where they have proven useful, such as recognition, control, prediction, and categorization tasks.
- The document presents a neural network model for recognizing handwritten digits. It uses a dataset of 20x20 pixel grayscale images of digits 0-9.
- The proposed neural network has an input layer of 400 nodes, a hidden layer of 25 nodes, and an output layer of 10 nodes. It is trained using backpropagation to classify images.
- The model achieves an accuracy of over 96.5% on test data after 200 iterations of training, outperforming a logistic regression model which achieved 91.5% accuracy. Future work could involve classifying more complex natural images.
the slides are aimed to give a brief introductory base to Neural Networks and its architectures. it covers logistic regression, shallow neural networks and deep neural networks. the slides were presented in Deep Learning IndabaX Sudan.
Multilayer Backpropagation Neural Networks for Implementation of Logic GatesIJCSES Journal
ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn
from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is
designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained
by using a backpropagation algorithm until the model satisfies the predefined error criteria (e)
which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The
NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For
OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
The document provides an overview of backpropagation, a common algorithm used to train multi-layer neural networks. It discusses:
- How backpropagation works by calculating error terms for output nodes and propagating these errors back through the network to adjust weights.
- The stages of feedforward activation and backpropagation of errors to update weights.
- Options like initial random weights, number of training cycles and hidden nodes.
- An example of using backpropagation to train a network to learn the XOR function over multiple training passes of forward passing and backward error propagation and weight updating.
This document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are inspired by biological neural networks and are composed of interconnected nodes that mimic neurons. ANNs use a learning process to update synaptic connection weights between nodes based on training data to perform tasks like pattern recognition. The document outlines the history of ANNs and covers popular applications. It also describes common ANN properties, architectures, and the backpropagation algorithm used for training multilayer networks.
This document provides an overview of neural networks and related topics. It begins with an introduction to neural networks and discusses natural neural networks, early artificial neural networks, modeling neurons, and network design. It then covers multi-layer neural networks, perceptron networks, training, and advantages of neural networks. Additional topics include fuzzy logic, genetic algorithms, clustering, and adaptive neuro-fuzzy inference systems (ANFIS).
The document discusses backpropagation, which is a popular neural network learning algorithm. It describes the key components of a neural network including the input, hidden, and output layers. During training, weights are adjusted to minimize error between the network's predictions and actual outputs. Backpropagation works by propagating error backwards from the output layer through hidden layers to update weights and biases using gradient descent. This helps the network learn and improve its ability to accurately predict the class labels of new input samples.
This document discusses neural networks and their learning capabilities. It describes how neural networks are composed of simple interconnected elements that can learn patterns from examples through training. Perceptrons are introduced as single-layer neural networks that can learn linearly separable functions through a simple learning rule. Multi-layer networks are shown to have greater learning capabilities than perceptrons using an algorithm called backpropagation that propagates errors backward through the network to update weights. Applications of neural networks include pattern recognition, control problems, and time series prediction tasks.
The document discusses neural networks and how they address limitations of polynomial hypotheses for classification tasks. It explains that neural networks use layers of simulated neurons to learn hierarchical representations of data. Lower layers of neurons in a neural network learn simple features that are combined in higher layers to learn more complex patterns and classify input data. This architecture allows neural networks to learn appropriate representations for tasks like computer vision without needing to explicitly define high-order polynomial features.
Artificial neural networks are computational models inspired by the human brain. They are composed of interconnected nodes that process information using a technique called machine learning. This report discusses the basic components of neural networks including neurons, layers, and training methods. It also provides examples of using neural networks to learn and implement simple logic functions like AND, OR, NAND, and NOR gates. The code shows how neural networks can be built and trained in MATLAB to recognize patterns in input data and produce the correct output.
Neural networks are computing systems inspired by the human brain that are composed of interconnected nodes similar to neurons. They can recognize complex patterns in raw data through learning algorithms. An artificial neural network consists of layers of nodes - an input layer, one or more hidden layers, and an output layer. Weights are assigned to connections between nodes and are adjusted during training to produce the desired output.
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 and applies an activation function to produce an output. ANNs can learn from examples through a process of adjusting the weights between neurons. Backpropagation is a common learning algorithm that propagates errors backward from the output to adjust weights and minimize errors. While single-layer perceptrons can only model linearly separable problems, multi-layer feedforward neural networks can handle non-linear problems using hidden layers that allow the network to learn complex patterns from data.
The document provides information about artificial neural networks and how they relate to biological neural networks. It discusses:
- The key components of biological neurons and how they transmit signals.
- How artificial neural networks are modeled after biological neural networks, with artificial neurons and weighted connections between them.
- The main types of neural network architectures - feedforward and recurrent networks. Feedforward networks have no feedback loops, while recurrent networks have feedback loops.
- How neural networks learn by adjusting the weights between neurons through various learning rules and algorithms, like Hebbian learning and backpropagation, to minimize error between the actual and desired output.
ANNs have been widely used in various domains for: Pattern recognition Funct...vijaym148
The document discusses artificial neural networks (ANNs), which are computational models inspired by the human brain. ANNs consist of interconnected nodes that mimic neurons in the brain. Knowledge is stored in the synaptic connections between neurons. ANNs can be used for pattern recognition, function approximation, and associative memory. Backpropagation is an important algorithm for training multilayer ANNs by adjusting the synaptic weights based on examples. ANNs have been applied to problems like image classification, speech recognition, and financial prediction.
This document discusses machine learning classification using a single layer feed forward neural network. It begins with definitions of machine learning and the different types of machine learning problems. Supervised learning classification is explained where the goal is to learn from labeled training data to classify new observations. Common classification algorithms and the components of a learning model are described. Finally, the document provides an example of how a single layer perceptron neural network can be used to classify a sample dataset into two classes by learning the optimal weights through an iterative process.
The document discusses neural networks and their ability to perform non-linear classification. It describes how neural networks can learn complex patterns in data through multiple layers of nonlinear transformations. The key algorithms covered are the forward pass to perform inference and the backward pass using backpropagation for learning network weights. Backpropagation efficiently computes gradients through the network to optimize weights with gradient descent. The document provides examples of network architecture, activation functions, loss functions, and the mathematical details of backpropagation for multi-layer neural networks.
The document discusses components and concepts related to artificial neural networks. It describes the basic units (neurons), connections between neurons, propagation and activation functions, common activation functions like sigmoid and tanh, and network topologies including feedforward and recurrent networks. It provides details on how artificial neural networks are designed based on the human brain and how information is processed through the connections and activation of neurons.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
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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Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
1. CSE 634
Data Mining Techniques
Presentationon Neural Network
Jalal Mahmud ( 105241140)
Hyung-Yeon, Gu(104985928)
Course Teacher : Prof. Anita Wasilewska
State University of New York at Stony Brook
3. Overview
Basics of Neural Network
Advanced Features of Neural Network
Applications I-II
Summary
4. Basics of Neural Network
What is a Neural Network
Neural Network Classifier
Data Normalization
Neuron and bias of a neuron
Single Layer Feed Forward
Limitation
Multi Layer Feed Forward
Back propagation
5. Neural Networks
What is a Neural Network?
Similarity with biological network
Fundamental processing elements of a neural network
is a neuron
1.Receives inputs from other source
2.Combines them in someway
3.Performs a generally nonlinear operation on the result
4.Outputs the final result
•Biologically motivated approach to
machine learning
6. Similarity with Biological Network
• Fundamental processing element of a
neural network is a neuron
• A human brain has 100 billion neurons
• An ant brain has 250,000 neurons
8. Neural Network
Neural Network is a set of connected
INPUT/OUTPUT UNITS, where each
connection has a WEIGHT associated with it.
Neural Network learning is also called
CONNECTIONIST learning due to the connections
between units.
It is a case of SUPERVISED, INDUCTIVE or
CLASSIFICATION learning.
9. Neural Network
Neural Network learns by adjusting the
weights so as to be able to correctly classify
the training data and hence, after testing
phase, to classify unknown data.
Neural Network needs long time for training.
Neural Network has a high tolerance to noisy
and incomplete data
10. Neural Network Classifier
Input: Classification data
It contains classification attribute
Data is divided, as in any classification problem.
[Training data and Testing data]
All data must be normalized.
(i.e. all values of attributes in the database are changed to
contain values in the internal [0,1] or[-1,1])
Neural Network can work with data in the range of (0,1) or (-1,1)
Two basic normalization techniques
[1] Max-Min normalization
[2] Decimal Scaling normalization
12. Example of Max-Min
Normalization
A
new
A
new
A
new
A
A
A
v
v min
_
)
min
_
max
_
(
min
max
min
'
Max- Min normalization formula
Example: We want to normalize data to range of the interval [0,1].
We put: new_max A= 1, new_minA =0.
Say, max A was 100 and min A was 20 ( That means maximum and minimum
values for the attribute ).
Now, if v = 40 ( If for this particular pattern , attribute value is 40 ), v’
will be calculated as , v’ = (40-20) x (1-0) / (100-20) + 0
=> v’ = 20 x 1/80
=> v’ = 0.4
13. Decimal Scaling Normalization
[2]Decimal Scaling Normalization
Normalization by decimal scaling normalizes by moving the
decimal point of values of attribute A.
j
v
v
10
'
Here j is the smallest integer such that max|v’|<1.
Example :
A – values range from -986 to 917. Max |v| = 986.
v = -986 normalize to v’ = -986/1000 = -0.986
14. One Neuron as a
Network
Here x1 and x2 are normalized attribute value of data.
y is the output of the neuron , i.e the class label.
x1 and x2 values multiplied by weight values w1 and w2 are input to the
neuron x.
Value of x1 is multiplied by a weight w1 and values of x2 is multiplied by
a weight w2.
Given that
• w1 = 0.5 and w2 = 0.5
• Say value of x1 is 0.3 and value of x2 is 0.8,
• So, weighted sum is :
• sum= w1 x x1 + w2 x x2 = 0.5 x 0.3 + 0.5 x 0.8 = 0.55
15. One Neuron as a Network
• The neuron receives the weighted sum as input and calculates
the output as a function of input as follows :
• y = f(x) , where f(x) is defined as
• f(x) = 0 { when x< 0.5 }
• f(x) = 1 { when x >= 0.5 }
• For our example, x ( weighted sum ) is 0.55, so y = 1 ,
• That means corresponding input attribute values are classified in
class 1.
• If for another input values , x = 0.45 , then f(x) = 0,
• so we could conclude that input values are classified to
class 0.
16. Bias of a Neuron
We need the bias value to be added to the weighted
sum ∑wixi so that we can transform it from the origin.
v = ∑wixi + b, here b is the bias
x1-x2=0
x1-x2= 1
x1
x2
x1-x2= -1
17. Bias as extra input
Input
Attribute
values
weights
Summing function
Activation
function
v
Output
class
y
x1
x2
xm
w2
wm
W1
)
(
w0
x0 = +1
b
w
x
w
v j
m
j
j
0
0
18. Neuron with Activation
The neuron is the basic information processing unit of a
NN. It consists of:
1 A set of links, describing the neuron inputs, with
weights W1, W2, …, Wm
2. An adder function (linear combiner) for computing the
weighted sum of the inputs (real numbers):
3 Activation function : for limiting the amplitude of the
neuron output.
m
1
j
jx
w
u
j
)
(u
y b
19. Why We Need Multi Layer ?
Linear Separable:
Linear inseparable:
Solution?
y
x y
x
y
x
20. k
O
jk
w
Output nodes
Input nodes
Hidden nodes
Output Class
Input Record : xi
wij - weights
Network is fully connected
j
O
A Multilayer Feed-Forward
Neural Network
21. Neural Network Learning
The inputs are fed simultaneously into the
input layer.
The weighted outputs of these units are fed
into hidden layer.
The weighted outputs of the last hidden layer
are inputs to units making up the output layer.
22. A Multilayer Feed Forward Network
The units in the hidden layers and output layer are
sometimes referred to as neurodes, due to their
symbolic biological basis, or as output units.
A network containing two hidden layers is called a
three-layer neural network, and so on.
The network is feed-forward in that none of the
weights cycles back to an input unit or to an output
unit of a previous layer.
23. A Multilayered Feed – Forward Network
INPUT: records without class attribute with
normalized attributes values.
INPUT VECTOR: X = { x1, x2, …. xn}
where n is the number of (non class) attributes.
INPUT LAYER – there are as many nodes as non-
class attributes i.e. as the length of the input vector.
HIDDEN LAYER – the number of nodes in the
hidden layer and the number of hidden layers
depends on implementation.
24. A Multilayered Feed–Forward
Network
OUTPUT LAYER – corresponds to the
class attribute.
There are as many nodes as classes
(values of the class attribute).
k
O k= 1, 2,.. #classes
• Network is fully connected, i.e. each unit provides input
to each unit in the next forward layer.
25. Classification by Back propagation
Back Propagation learns by iteratively
processing a set of training data (samples).
For each sample, weights are modified to
minimize the error between network’s
classification and actual classification.
26. Steps in Back propagation
Algorithm
STEP ONE: initialize the weights and biases.
The weights in the network are initialized to
random numbers from the interval [-1,1].
Each unit has a BIAS associated with it
The biases are similarly initialized to random
numbers from the interval [-1,1].
STEP TWO: feed the training sample.
27. Steps in Back propagation Algorithm
( cont..)
STEP THREE: Propagate the inputs forward;
we compute the net input and output of each
unit in the hidden and output layers.
STEP FOUR: back propagate the error.
STEP FIVE: update weights and biases to
reflect the propagated errors.
STEP SIX: terminating conditions.
28. Propagation through Hidden
Layer ( One Node )
The inputs to unit j are outputs from the previous layer. These are
multiplied by their corresponding weights in order to form a
weighted sum, which is added to the bias associated with unit j.
A nonlinear activation function f is applied to the net input.
-
f
weighted
sum
Input
vector x
output y
Activation
function
weight
vector
w
w0j
w1j
wnj
x0
x1
xn
Bias j
29. Propagate the inputs forward
For unit j in the input layer, its output is
equal to its input, that is,
j
j I
O
for input unit j.
• The net input to each unit in the hidden and output
layers is computed as follows.
•Given a unit j in a hidden or output layer, the net input is
i
j
i
ij
j O
w
I
where wij is the weight of the connection from unit i in the previous layer to
unit j; Oi is the output of unit I from the previous layer;
j
is the bias of the unit
30. Propagate the inputs forward
Each unit in the hidden and output layers takes its
net input and then applies an activation function.
The function symbolizes the activation of the
neuron represented by the unit. It is also called a
logistic, sigmoid, or squashing function.
Given a net input Ij to unit j, then
Oj = f(Ij),
the output of unit j, is computed as j
I
j
e
O
1
1
31. Back propagate the error
When reaching the Output layer, the error is
computed and propagated backwards.
For a unit k in the output layer the error is
computed by a formula:
)
)(
1
( k
k
k
k
k O
T
O
O
Err
•
Where O k – actual output of unit k ( computed by activation
function.
Tk – True output based of known class label; classification of
training sample
Ok(1-Ok) – is a Derivative ( rate of change ) of activation function.
k
I
k
e
O
1
1
32. Back propagate the error
The error is propagated backwards by updating
weights and biases to reflect the error of the
network classification .
For a unit j in the hidden layer the error is
computed by a formula:
•
jk
k
k
j
j
j w
Err
O
O
Err
)
1
(
where wjk is the weight of the connection from unit j to unit
k in the next higher layer, and Errk is the error of unit k.
33. Update weights and biases
Weights are updated by the following equations,
where l is a constant between 0.0 and 1.0
reflecting the learning rate, this learning rate is
fixed for implementation.
i
j
ij O
Err
l
w )
(
ij
ij
ij w
w
w
• Biases are updated by the following equations
j
j
j
j
j Err
l)
(
34. Update weights and biases
We are updating weights and biases after the
presentation of each sample.
This is called case updating.
Epoch --- One iteration through the training set is called an
epoch.
Epoch updating ------------
Alternatively, the weight and bias increments could be
accumulated in variables and the weights and biases
updated after all of the samples of the training set have
been presented.
Case updating is more accurate
35. Terminating Conditions
Training stops
ij
w
• All in the previous epoch are below some
threshold, or
•The percentage of samples misclassified in the previous
epoch is below some threshold, or
• a pre specified number of epochs has expired.
• In practice, several hundreds of thousands of epochs may
be required before the weights will converge.
36. Output nodes
Input nodes
Hidden nodes
Output vector
Input vector: xi
wij
i
j
i
ij
j O
w
I
)
)(
1
( k
k
k
k
k O
T
O
O
Err
jk
k
k
j
j
j w
Err
O
O
Err
)
1
(
i
j
ij
ij O
Err
l
w
w )
(
j
j
j Err
l)
(
j
I
j
e
O
1
1
Backpropagation Formulas
37. Example of Back propagation
x1 x2 x3 w14 w15 w24 w25 w34 w35 w46 w56
1 0 1 0.2 -0.3 0.4 0.1 -0.5 0.2 -0.3 -0.2
Initial Input and weight
Initialize weights :
Input = 3, Hidden
Neuron = 2 Output =1
Random Numbers
from -1.0 to 1.0
38. Example ( cont.. )
Bias added to Hidden
+ Output nodes
Initialize Bias
Random Values from
-1.0 to 1.0
Bias ( Random )
θ4 θ5 θ6
-0.4 0.2 0.1
39. Net Input and Output Calculation
Unitj Net Input Ij Output Oj
4 0.2 + 0 + 0.5 -0.4 = -0.7
5 -0.3 + 0 + 0.2 + 0.2 =0.1
6 (-0.3)0.332-
(0.2)(0.525)+0.1= -0.105
1
.
0
1
1
e
Oj
7
.
0
1
1
e
Oj
105
.
0
1
1
e
Oj
= 0.332
= 0.525
= 0.475
40. Calculation of Error at Each
Node
Unit j Error j
6 0.475(1-0.475)(1-0.475) =0.1311
We assume T 6 = 1
5 0.525 x (1- 0.525)x 0.1311x
(-0.2) = 0.0065
4 0.332 x (1-0.332) x 0.1311 x
(-0.3) = -0.0087
42. Advanced Features of Neural
Network
Training with Subsets
Modular Neural Network
Evolution of Neural Network
43. Variants of Neural Networks
Learning
Supervised learning/Classification
• Control
• Function approximation
• Associative memory
Unsupervised learning or Clustering
44. Training with Subsets
Select subsets of data
Build new classifier on subset
Aggregate with previous classifiers
Compare error after adding classifier
Repeat as long as error decreases
45. Training with subsets
Subset 1
Subset 2
Subset 3
Subset n
NN 1
NN 2
NN 3
NN n
A Single
Neural Network
Model
The
Whole
Dataset
Split the dataset
into subsets
that can fit
into memory
.
.
.
46. Modular Neural Network
Modular Neural Network
• Made up of a combination of several neural
networks.
The idea is to reduce the load for each neural
network as opposed to trying to solve the
problem on a single neural network.
47. Evolving Network Architectures
Small networks without a hidden layer can’t
solve problems such as XOR, that are not
linearly separable.
•Large networks can easily overfit a problem
to match the training data, limiting their
ability to generalize a problem set.
48. Constructive vs Destructive
Algorithm
Constructive algorithms take a minimal
network and build up new layers nodes and
connections during training.
Destructive algorithms take a maximal
network and prunes unnecessary layers
nodes and connections during training.
49. Training Process of the MLP
The training will be continued until the RMS is
minimized.
Global Minimum
Local Minimum
Local Minimum
ERROR
W (N dimensional)
50. Faster Convergence
Back prop requires many epochs to converge
Some ideas to overcome this
• Stochastic learning
• Update weights after each training example
• Momentum
• Add fraction of previous update to current update
• Faster convergence
51. Applications-I
Handwritten Digit Recognition
Face recognition
Time series prediction
Process identification
Process control
Optical character recognition
52. Application-II
Forecasting/Market Prediction: finance and banking
Manufacturing: quality control, fault diagnosis
Medicine: analysis of electrocardiogram data, RNA & DNA
sequencing, drug development without animal testing
Control: process, robotics
53. Summary
We presented mainly the followings-------
Basic building block of Artificial Neural Network.
Construction , working and limitation of single layer neural
network (Single Layer Neural Network).
Back propagation algorithm for multi layer feed forward NN.
Some Advanced Features like training with subsets, Quicker
convergence, Modular Neural Network, Evolution of NN.
Application of Neural Network.
54. Remember…..
ANNs perform well, generally better with larger number of
hidden units
More hidden units generally produce lower error
Determining network topology is difficult
Choosing single learning rate impossible
Difficult to reduce training time by altering the network
topology or learning parameters
NN(Subset) often produce better results