Neural networks are inspired by biological neural systems. An artificial neural network (ANN) is an information processing paradigm that is modeled after the human brain. ANNs learn by example, through a learning process, like the way synapses strengthen in the human brain. An ANN is composed of interconnected processing nodes that work together to solve problems. It can be trained to perform tasks by considering examples without being explicitly programmed.
This document provides an overview of self-organizing maps (SOM) as an unsupervised learning technique. It discusses the principles of self-organization including self-amplification, competition, and cooperation. The Willshaw-von der Malsburg model and Kohonen feature maps are presented as two approaches to building topographic maps through self-organization. The Kohonen SOM learning algorithm is described as involving competition between neurons to determine a winning neuron, cooperation between neighboring neurons, and adaptive changes to synaptic weights based on Hebbian learning principles.
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
1. Machine learning involves developing algorithms that can learn from data and improve their performance over time without being explicitly programmed. 2. Neural networks are a type of machine learning algorithm inspired by the human brain that can perform both supervised and unsupervised learning tasks. 3. Supervised learning involves using labeled training data to infer a function that maps inputs to outputs, while unsupervised learning involves discovering hidden patterns in unlabeled data through techniques like clustering.
- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets.
- It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted.
- It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.
Neural networks can be biological models of the brain or artificial models created through software and hardware. The human brain consists of interconnected neurons that transmit signals through connections called synapses. Artificial neural networks aim to mimic this structure using simple processing units called nodes that are connected by weighted links. A feed-forward neural network passes information in one direction from input to output nodes through hidden layers. Backpropagation is a common supervised learning method that uses gradient descent to minimize error by calculating error terms and adjusting weights between layers in the network backwards from output to input. Neural networks have been applied successfully to problems like speech recognition, character recognition, and autonomous vehicle navigation.
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
This document provides an overview of self-organizing maps (SOM) as an unsupervised learning technique. It discusses the principles of self-organization including self-amplification, competition, and cooperation. The Willshaw-von der Malsburg model and Kohonen feature maps are presented as two approaches to building topographic maps through self-organization. The Kohonen SOM learning algorithm is described as involving competition between neurons to determine a winning neuron, cooperation between neighboring neurons, and adaptive changes to synaptic weights based on Hebbian learning principles.
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
1. Machine learning involves developing algorithms that can learn from data and improve their performance over time without being explicitly programmed. 2. Neural networks are a type of machine learning algorithm inspired by the human brain that can perform both supervised and unsupervised learning tasks. 3. Supervised learning involves using labeled training data to infer a function that maps inputs to outputs, while unsupervised learning involves discovering hidden patterns in unlabeled data through techniques like clustering.
- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets.
- It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted.
- It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.
Neural networks can be biological models of the brain or artificial models created through software and hardware. The human brain consists of interconnected neurons that transmit signals through connections called synapses. Artificial neural networks aim to mimic this structure using simple processing units called nodes that are connected by weighted links. A feed-forward neural network passes information in one direction from input to output nodes through hidden layers. Backpropagation is a common supervised learning method that uses gradient descent to minimize error by calculating error terms and adjusting weights between layers in the network backwards from output to input. Neural networks have been applied successfully to problems like speech recognition, character recognition, and autonomous vehicle navigation.
The document discusses the K-nearest neighbors (KNN) algorithm, a simple machine learning algorithm used for classification problems. KNN works by finding the K training examples that are closest in distance to a new data point, and assigning the most common class among those K examples as the prediction for the new data point. The document covers how KNN calculates distances between data points, how to choose the K value, techniques for handling different data types, and the strengths and weaknesses of the KNN algorithm.
Neural networks are computational models inspired by the human brain. They consist of interconnected nodes that process information using a principle called neural learning. The document discusses the history and evolution of neural networks. It also provides examples of applications like image recognition, medical diagnosis, and predictive analytics. Neural networks are well-suited for problems that are difficult to solve with traditional algorithms like pattern recognition and classification.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
The document provides an overview of self-organizing maps (SOM). It defines SOM as an unsupervised learning technique that reduces the dimensions of data through the use of self-organizing neural networks. SOM is based on competitive learning where the closest neural network unit to the input vector (the best matching unit or BMU) is identified and adjusted along with neighboring units. The algorithm involves initializing weight vectors, presenting input vectors, identifying the BMU, and updating weights of the BMU and neighboring units. SOM can be used for applications like dimensionality reduction, clustering, and visualization.
Support Vector Machine ppt presentationAyanaRukasar
Support vector machines (SVM) is a supervised machine learning algorithm used for both classification and regression problems. However, it is primarily used for classification. The goal of SVM is to create the best decision boundary, known as a hyperplane, that separates clusters of data points. It chooses extreme data points as support vectors to define the hyperplane. SVM is effective for problems that are not linearly separable by transforming them into higher dimensional spaces. It works well when there is a clear margin of separation between classes and is effective for high dimensional data. An example use case in Python is presented.
This document provides an overview of artificial neural networks and their application as a model of the human brain. It discusses the biological neuron, different types of neural networks including feedforward, feedback, time delay, and recurrent networks. It also covers topics like learning in perceptrons, training algorithms, applications of neural networks, and references key concepts like connectionism, associative memory, and massive parallelism in the brain.
K-medoids is a clustering algorithm that groups similar data points into K clusters by selecting representative data points called medoids. It iteratively assigns data points to the closest medoid and updates the medoids to minimize distances between points and clusters. K-medoids is more robust to outliers than K-means and can handle non-Euclidean distances, making it useful for clustering categorical or nonlinear data. It has various applications but is more computationally expensive than K-means.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
This presentation introduces naive Bayesian classification. It begins with an overview of Bayes' theorem and defines a naive Bayes classifier as one that assumes conditional independence between predictor variables given the class. The document provides examples of text classification using naive Bayes and discusses its advantages of simplicity and accuracy, as well as its limitation of assuming independence. It concludes that naive Bayes is a commonly used and effective classification technique.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Fundamental, An Introduction to Neural NetworksNelson Piedra
This document provides an introduction to neural networks. It discusses how the first wave of interest emerged after McCullock and Pitts introduced simplified neuron models in 1943. However, perceptron models were shown to have deficiencies in 1969, leading to reduced funding and many researchers leaving the field. Interest re-emerged in the early 1980s after important theoretical results like backpropagation and new hardware increased processing capacities. The document then describes key components of artificial neural networks, including processing units that receive inputs and propagate outputs, different types of connections between units, and activation and output rules. It also covers different network topologies like feed-forward and recurrent networks.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
This Naive Bayes Tutorial from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial:
1. What is Machine Learning?
2. Introduction to Classification
3. Classification Algorithms
4. What is Naive Bayes?
5. Use Cases of Naive Bayes
6. Demo – Employee Salary Prediction in R
This document provides an overview of Chapter 14 on probabilistic reasoning and Bayesian networks from an artificial intelligence textbook. It introduces Bayesian networks as a way to represent knowledge over uncertain domains using directed graphs. Each node corresponds to a variable and arrows represent conditional dependencies between variables. The document explains how Bayesian networks can encode a joint probability distribution and represent conditional independence relationships. It also discusses techniques for efficiently representing conditional distributions in Bayesian networks, including noisy logical relationships and continuous variables. The chapter covers exact and approximate inference methods for Bayesian networks.
Artificial neural networks are a form of artificial intelligence inspired by biological neural networks. They are composed of interconnected processing units that can learn patterns from data through training. Neural networks are well-suited for tasks like pattern recognition, classification, and prediction. They learn by example without being explicitly programmed, similarly to how the human brain learns.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
This document discusses evaluating hypotheses and estimating hypothesis accuracy. It provides the following key points:
- The accuracy of a hypothesis estimated from a training set may be different from its true accuracy due to bias and variance. Testing the hypothesis on an independent test set provides an unbiased estimate.
- Given a hypothesis h that makes r errors on a test set of n examples, the sample error r/n provides an unbiased estimate of the true error. The variance of this estimate depends on r and n based on the binomial distribution.
- For large n, the binomial distribution can be approximated by the normal distribution. Confidence intervals for the true error can then be determined based on the sample error and standard deviation
Artificial neural networks (ANNs) are processing systems inspired by biological neural networks. They consist of interconnected processing elements that dynamically change their outputs based on external inputs. While much simpler than actual brains, some ANNs have accurately modeled systems like the retina. ANNs are initially trained on large datasets to learn input-output relationships, then make predictions on new inputs. They are nonlinear, adaptable systems suited for parallel processing tasks.
Neural networks are computational models inspired by the human brain. They consist of interconnected nodes that process information using a principle called neural learning. The document discusses the history and evolution of neural networks. It also provides examples of applications like image recognition, medical diagnosis, and predictive analytics. Neural networks are well-suited for problems that are difficult to solve with traditional algorithms like pattern recognition and classification.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
The document provides an overview of self-organizing maps (SOM). It defines SOM as an unsupervised learning technique that reduces the dimensions of data through the use of self-organizing neural networks. SOM is based on competitive learning where the closest neural network unit to the input vector (the best matching unit or BMU) is identified and adjusted along with neighboring units. The algorithm involves initializing weight vectors, presenting input vectors, identifying the BMU, and updating weights of the BMU and neighboring units. SOM can be used for applications like dimensionality reduction, clustering, and visualization.
Support Vector Machine ppt presentationAyanaRukasar
Support vector machines (SVM) is a supervised machine learning algorithm used for both classification and regression problems. However, it is primarily used for classification. The goal of SVM is to create the best decision boundary, known as a hyperplane, that separates clusters of data points. It chooses extreme data points as support vectors to define the hyperplane. SVM is effective for problems that are not linearly separable by transforming them into higher dimensional spaces. It works well when there is a clear margin of separation between classes and is effective for high dimensional data. An example use case in Python is presented.
This document provides an overview of artificial neural networks and their application as a model of the human brain. It discusses the biological neuron, different types of neural networks including feedforward, feedback, time delay, and recurrent networks. It also covers topics like learning in perceptrons, training algorithms, applications of neural networks, and references key concepts like connectionism, associative memory, and massive parallelism in the brain.
K-medoids is a clustering algorithm that groups similar data points into K clusters by selecting representative data points called medoids. It iteratively assigns data points to the closest medoid and updates the medoids to minimize distances between points and clusters. K-medoids is more robust to outliers than K-means and can handle non-Euclidean distances, making it useful for clustering categorical or nonlinear data. It has various applications but is more computationally expensive than K-means.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
This presentation introduces naive Bayesian classification. It begins with an overview of Bayes' theorem and defines a naive Bayes classifier as one that assumes conditional independence between predictor variables given the class. The document provides examples of text classification using naive Bayes and discusses its advantages of simplicity and accuracy, as well as its limitation of assuming independence. It concludes that naive Bayes is a commonly used and effective classification technique.
This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Fundamental, An Introduction to Neural NetworksNelson Piedra
This document provides an introduction to neural networks. It discusses how the first wave of interest emerged after McCullock and Pitts introduced simplified neuron models in 1943. However, perceptron models were shown to have deficiencies in 1969, leading to reduced funding and many researchers leaving the field. Interest re-emerged in the early 1980s after important theoretical results like backpropagation and new hardware increased processing capacities. The document then describes key components of artificial neural networks, including processing units that receive inputs and propagate outputs, different types of connections between units, and activation and output rules. It also covers different network topologies like feed-forward and recurrent networks.
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
This Naive Bayes Tutorial from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial:
1. What is Machine Learning?
2. Introduction to Classification
3. Classification Algorithms
4. What is Naive Bayes?
5. Use Cases of Naive Bayes
6. Demo – Employee Salary Prediction in R
This document provides an overview of Chapter 14 on probabilistic reasoning and Bayesian networks from an artificial intelligence textbook. It introduces Bayesian networks as a way to represent knowledge over uncertain domains using directed graphs. Each node corresponds to a variable and arrows represent conditional dependencies between variables. The document explains how Bayesian networks can encode a joint probability distribution and represent conditional independence relationships. It also discusses techniques for efficiently representing conditional distributions in Bayesian networks, including noisy logical relationships and continuous variables. The chapter covers exact and approximate inference methods for Bayesian networks.
Artificial neural networks are a form of artificial intelligence inspired by biological neural networks. They are composed of interconnected processing units that can learn patterns from data through training. Neural networks are well-suited for tasks like pattern recognition, classification, and prediction. They learn by example without being explicitly programmed, similarly to how the human brain learns.
Machine Learning With Logistic RegressionKnoldus Inc.
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
This document discusses evaluating hypotheses and estimating hypothesis accuracy. It provides the following key points:
- The accuracy of a hypothesis estimated from a training set may be different from its true accuracy due to bias and variance. Testing the hypothesis on an independent test set provides an unbiased estimate.
- Given a hypothesis h that makes r errors on a test set of n examples, the sample error r/n provides an unbiased estimate of the true error. The variance of this estimate depends on r and n based on the binomial distribution.
- For large n, the binomial distribution can be approximated by the normal distribution. Confidence intervals for the true error can then be determined based on the sample error and standard deviation
Artificial neural networks (ANNs) are processing systems inspired by biological neural networks. They consist of interconnected processing elements that dynamically change their outputs based on external inputs. While much simpler than actual brains, some ANNs have accurately modeled systems like the retina. ANNs are initially trained on large datasets to learn input-output relationships, then make predictions on new inputs. They are nonlinear, adaptable systems suited for parallel processing tasks.
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.
- In 1975, Kunihiko Fukushima introduced the Cognitron network, which was an extension of the original perceptron and was able to handle pattern recognition problems better than the perceptron.
- The Cognitron used multiple layers of convergent subcircuits that allowed it to discriminate between patterns to some degree, unlike the perceptron.
- Fukushima later modified the Cognitron into the Neocognitron in 1980 by adding additional summation nodes, which made the network able to recognize patterns regardless of their position in the visual field.
The document discusses software quality assurance plans and methods. It defines quality, describes quality control and assurance activities like inspections, reviews and testing. It explains factors that affect quality like correctness, reliability, maintainability. Methods to assure quality discussed are verification and validation, inspections, reviews, and static analysis. The document also covers project monitoring plans and tools, software design fundamentals, objectives of design, design principles and strategies.
The document discusses software configuration management. It describes SCM as identifying, monitoring, and controlling changes made to software items during maintenance. SCM manages software configuration items (SCIs) which comprise all information produced during software development. As development progresses, SCIs increase rapidly so SCM is needed to manage and control them. SCM identifies changes, ensures proper implementation of changes, and reports on changes made. It aims to maximize productivity by minimizing errors.
The document discusses personnel planning and team structures for software engineering projects. It describes staffing as involving hiring personnel, defining requirements, recruiting, compensating, and developing employees. Personnel planning involves estimating effort and schedules for subsystems and modules to determine staffing needs over the project duration. Different team structures are also outlined, including ego-less teams, chief programmer teams, and controlled decentralized teams. Advantages and disadvantages of each structure are provided.
This document is the preface to a book on software engineering published by Rozy Computech Services. It provides contact information for Rozy Computech Services and acknowledges contributions to revising the book. The preface outlines the book's 9 chapters which cover topics such as software and software engineering, planning software projects, software configuration management, software requirements specifications, design and implementation, reliability, testing, maintenance, and CASE tools. It aims to acquaint students with basic software engineering concepts and current tools and techniques.
The document discusses normalization in relational databases. It defines some key concepts like functional dependencies, normal forms, and anomalies like insertion and deletion anomalies. It explains how normalization aims to eliminate anomalies by decomposing relations and placing attributes together that are closely related based on functional dependencies. The goal of normalization is to produce a stable and flexible database design with relations that faithfully represent the enterprise data.
The document discusses normalization and different normal forms. It defines normalization as refining the database design to remove anomalies by segregating data over multiple relations. The key points covered include:
- The need for normalization to improve design, reduce redundancy, and achieve consistency by removing modification anomalies.
- First normal form requires each attribute contain a single value. Issues like deletion, insertion, and update anomalies can still occur in 1NF.
- Second normal form eliminates anomalies caused by non-key attributes depending on part of a composite key.
- Third normal form removes transitive dependencies and anomalies caused by overlapping candidate keys.
C lecture 4 nested loops and jumping statements slideshareGagan Deep
Nested Loops and Jumping Statements(Loop Control Statements), Goto statement in C, Return Statement in C Exit statement in C, For Loops with Nested Loops, While Loop with Nested Loop, Do-While Loop with Nested Loops, Break Statement, Continue Statement : visit us at : www.rozyph.com
This document is the preface to a book on Artificial Intelligence published by Rozy Publishing House. It provides an overview of the book's contents and development process. The book contains 10 chapters that cover topics such as problem representation, structured knowledge, rule-based systems, logic, expert systems, learning techniques, search strategies, and PROLOG programming. It was created over two years by authors and academic experts to provide relevant study material for undergraduate and postgraduate AI courses. Feedback from readers is welcomed so the book can be improved in future publications.
The document provides an overview of SQL and its characteristics. It discusses that SQL is a standard language for relational database management systems and provides a high-level declarative interface. The document also describes the different components of SQL including data definition language, data manipulation language, and data control language. It provides examples of creating tables and databases, inserting and querying data, and other SQL statements.
This document discusses Management Information Systems (MIS). It defines MIS as systems that produce information for management at different levels to support operations, planning, control, and decision making. While computers are not essential for MIS, they have made it possible to handle large data volumes quickly and accurately. The document also discusses the difference between data and information, with information being relevant knowledge produced from processed data. It provides examples of different types of information systems like Transaction Processing Systems, Management Information Systems, and Decision Support Systems that support different management levels.
The document discusses the systems analysis and design process for developing systems like a Management Information System (MIS). It describes the key stages in the systems development life cycle, including problem recognition, feasibility study, systems analysis, design, testing, implementation, and maintenance. It provides details on various techniques and considerations used at each stage, such as classifying problem types during problem recognition, assessing technical, operational, and economic feasibility, gathering requirements, and designing system components. The iterative nature of systems development is also emphasized.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
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.
The document provides biographical information about Professor Kunihiko Fukushima, a pioneer in the field of neural networks. It describes his invention of the Neocognitron, a hierarchical neural network for deformation invariant pattern recognition. The Neocognitron is able to recognize patterns that have been distorted through partial shifts, rotations, or other transformations. The document also discusses Fukushima's research interests in modeling neural networks to understand visual processing and active vision in the brain.
Brain-computer interface (BCI) technology allows direct communication between the brain and an external device, enabling control of things in the physical world using thought alone. BCI systems work by detecting electrical brain signals using technologies like EEG, analyzing the signals to extract meaningful features, and translating the features into commands to control devices. Current research aims to develop non-invasive BCI methods to help those with disabilities like ALS regain control and independence.
Detail The Components Of A Synapse And Describe The...Jennifer Perry
Synapses allow neurons to communicate by transmitting chemical and electrical signals. They connect the axon of one neuron to the dendrites of other neurons. The brain contains around 100 billion neurons, each with around 7,000 synaptic connections. This vast network of interconnecting neurons underlies all of our cognitive functions and behaviors. At the microscopic level, a synapse contains release sites on the presynaptic axon terminal and receptor sites on the postsynaptic dendrite, separated by a narrow gap called the synaptic cleft. Neurotransmitters are released from vesicles in the presynaptic terminal and diffuse across the cleft to bind receptors, transmitting the signal to the next neuron.
Neurons communicate through synaptic connections. Studies have found that autistic brains exhibit overgrowth of neurons early in development, with the prefrontal cortex showing a 67% increase in neurons in autistic 2-year-olds compared to normal brains. This rapid growth occurs primarily in the first year of life in autism, rather than the slower development until age 2 seen in normal brains. This early overgrowth influences increased brain volume and grey and white matter in autistic children by age 3.
This document summarizes research on neurons and memory in the human brain. It discusses how neurons are the basic functional units of the brain and how they store and transmit information. Each neuron contains a cell body, dendrites that receive signals, and an axon that transmits signals to other neurons via synapses. There are over 100 billion neurons in the human brain, connected by trillions of synapses that are involved in memory storage. While the exact memory capacity of the human brain is unknown, estimates suggest it is at least in the petabytes, far exceeding any modern computer. Memory in the brain is stored via changes in synaptic connections between widely distributed networks of neurons rather than in any single brain region.
The neuron is the basic building block of the nervous system, consisting of a cell body, dendrites, an axon, and terminal buttons. Neurons communicate via electrical and chemical signals. The brain develops rapidly after birth through processes like neuron and synapse growth and synaptic pruning, which refines connections. Early childhood is critical for brain development as experience shapes pruning and the formation of over 15,000 new connections per neuron. The brain remains plastic and can form new connections if damaged.
This document discusses the structure and function of neurons. It begins by explaining that neurons are cells that communicate via synapses to transmit electrical or chemical signals. It then describes the key parts of a neuron - the cell body, dendrites, and axon. The document notes that glial cells support and insulate neurons. It compares the central and peripheral nervous systems. Finally, it discusses several neurological disorders related to neuronal dysfunction like cerebral palsy and essential tremor.
The document discusses the nervous system and immune system. It provides information on the types of cells in the nervous system including neurons, glial cells, astrocytes, oligodendrocytes and microglia. It describes how neurons transmit signals through electrical impulses and the role of synapses. The immune system sections covers the different blood cells, organs of the immune system like the spleen and lymph nodes, and how the lymphatic system connects these organs to monitor the body for invading microbes.
The document summarizes key concepts in neuroscience and behavior, including:
1) Plato correctly placed the mind in the brain, while Aristotle believed it was in the heart. Today we understand mind and brain are interconnected.
2) In the 1800s Franz Gall suggested bumps on the skull represented mental abilities, introducing the idea that abilities are modular in the brain.
3) Neurons are the basic building blocks of the nervous system and communicate via electrical and chemical signals.
4) The brain and spinal cord make up the central nervous system, while sensory and motor neurons comprise the peripheral nervous system.
This document provides an overview of neuroscience and the nervous system. It discusses the structure and function of neurons, how they communicate via neurotransmitters, and the basic anatomy and physiology of the brain and nervous system. Key points covered include the peripheral and central nervous systems, the endocrine system, and structures within the brain like the cortex, limbic system, and hemispheres. It also discusses various techniques for studying the brain like EEG, PET scans, and MRI scans.
This document outlines the agenda and content for a seminar on cognitive neuroscience. It introduces cognitive neuroscience as the study of biological substrates underlying cognition, focusing on the neural substrate of mental processes. It discusses the basic unit of the brain (the neuron), cognition, neurocognition, areas of the brain like the hippocampus and prefrontal cortex. It also outlines methods used to study cognition like psychophysics, EEG, fMRI, and transcranial magnetic stimulation. The seminar aims to provide an understanding of how psychological/cognitive functions are produced by neural circuits in the brain.
The document provides an overview of the nervous system, including its structure and function. It discusses the key components of the nervous system such as neurons, glial cells, the central nervous system, peripheral nervous system, and neurons and synapses. It describes the nervous system's role in controlling the body by processing sensory information and coordinating responses. The document also examines neural circuits and systems, as well as reflexes and other stimulus-response circuits in the nervous system.
Neurons are the basic structural and functional units of the nervous system. They have three main parts - a cell body containing the nucleus, dendrites which receive signals, and an axon which transmits signals. Neurons communicate via electrical and chemical signals across synapses. There are different types of neurons classified by structure and function, including sensory neurons, motor neurons, and interneurons. Neurons are formed through neurogenesis, migrate to their destinations, differentiate, and form neural networks. Diseases can cause neuron death. Key neurotransmitters mediate signaling between neurons.
Neurons are the basic building blocks of the nervous system and specialized to transmit information throughout the body chemically and electrically. They have a cell body that receives signals from dendrites and transmits signals down the axon via an axon hillock. At the end of the axon are terminal buttons that release neurotransmitters across synapses to communicate with other neurons. There are three main types of neurons - sensory, motor, and interneurons.
The document summarizes key concepts about the biology of the mind from Chapter 2 of Psychology (9th edition) by David Myers. It discusses (1) how neurons communicate via action potentials and neurotransmitters, (2) the structure and function of different parts of the brain and nervous system including the cortex, limbic system, and endocrine system, and (3) experimental techniques used to study the brain such as EEG, PET scans, and MRI.
The document provides information about soft computing techniques and artificial neural networks. It contains the following key points:
1. It introduces soft computing techniques such as neural networks, fuzzy logic, and genetic algorithms. It recommends books on these topics.
2. It discusses the biological neural network in the human brain and its characteristics such as the ability to learn and generalize knowledge.
3. It describes the goal of artificial neural networks is to simulate the human brain for functions like planning, thought, and speech recognition.
4. It outlines the basic biological components of neurons like the cell body, dendrites, axon, and synapse. It also introduces the characteristics of artificial neural networks and different neural network models like
The document provides an overview of neuroscience topics including:
- Neurons communicate via electrical and chemical signals like neurotransmitters. The nervous system processes information at cellular to social levels.
- The brain is divided into the central and peripheral nervous systems. The central nervous system includes the brain and spinal cord while the peripheral connects them to the body.
- Older brain structures like the brainstem control basic functions. The limbic system is involved in emotions. The cerebral cortex enables complex cognition.
This document discusses neurons and brain imaging techniques. It provides information on the basic structure and function of neurons, including the cell body, axon, dendrites and synapses. It also covers different types of neurons and neurotransmitters. The document then discusses several common brain imaging techniques used in neuroscience, including fMRI, CT, PET and EEG scans. It provides brief descriptions of how each technique works and what type of information it can provide about brain structure and function.
The document summarizes the nervous system and endocrine system. It describes how neurons are the basic building blocks that receive, transmit, and pass on electrochemical signals. It explains neural transmission through action potentials, neurotransmitters, and the synapse. The central and peripheral nervous systems are identified along with their roles in processing and relaying sensory and motor information. The endocrine system is introduced as another communication system that transmits chemical messengers like hormones through the bloodstream to target organs and tissues.
The document discusses software risk management and project scheduling. It defines risk as potential problems that could threaten a project's success but have not occurred yet. Risk management identifies, addresses, and eliminates these risks proactively. The document also discusses typical software risks, strategies to reduce risks, and tools for project scheduling like PERT charts, timeline charts, and Gantt charts. These tools help compartmentalize tasks, determine dependencies and allocate time to create a project schedule.
The document discusses software cost estimation and planning. It describes several models for software cost estimation including COCOMO and Putnam models. COCOMO uses staff months and lines of code to initially estimate effort which is then adjusted based on cost drivers. Putnam uses a Rayleigh curve staffing model based on volume, difficulty, and time constraints. Thorough planning is important to software projects and factors like life cycle, quality assurance, and risk management should be considered. Historical data and validated models can help produce more accurate cost and schedule estimates.
This document provides information about arrays in C programming. It defines an array as a linear list of homogeneous elements stored in consecutive memory locations. It notes that arrays always start at index 0 and end at size-1. It describes one-dimensional and multi-dimensional arrays. For one-dimensional arrays, it provides examples of declaration, definition, accessing elements, and several programs for operations like input, output, finding the largest element, and linear search. For multi-dimensional arrays, it describes how they are represented and defined with multiple subscript variables. It also includes a program to add two matrices as an example of a two-dimensional array.
C lecture 3 control statements slideshareGagan Deep
The document discusses different types of loops in programming languages that are used for repetition of tasks. It describes while, do-while and for loops as the three main types of loops. While and do-while loops are conditional loops that check a condition each time before repeating the code block. For loops allow repetition for a set number of times using three expressions for initialization, condition and increment. Some examples are provided to demonstrate the use of these loops to print numbers from 1 to 10.
The document discusses systems analysis and design. It defines a system as a group of integrated parts that work together to achieve a common objective. There are different types of systems such as deterministic, probabilistic, closed, and open systems. A system analyst studies systems to understand how their parts interact and achieve objectives. The analyst then works to improve system efficiency by assessing problems and providing alternative solutions. Control mechanisms are important for systems to self-correct when outputs deviate from standards. The analyst acts as a liaison between users and technology to enhance system performance.
Boolean algebra was developed by George Boole and applied to electrical circuits by Claude Shannon. It uses logical operators like AND, OR, and NOT to represent logical statements that are either true or false. Boolean algebra represents the states of electrical components like switches that are either open or closed. Circuits with switches in series represent AND operations, while circuits with switches in parallel represent OR operations. Boolean algebra expresses logical relationships using variables, operators, and equations in sum-of-products or product-of-sums form. It provides a mathematical foundation for analyzing electrical circuits and digital logic.
PL/SQL is a procedural language extension for SQL and the Oracle relational database. It allows developers to perform transactions in an Oracle database, define and control cursors, handle exceptions, and provide a host language for SQL. PL/SQL code is organized into logical blocks with optional declaration, mandatory executable, and optional exception handling sections. It provides benefits like improved performance, portability, and integration with SQL.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
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.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
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.
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 slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
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.
3. Artificial
Made or produced by human beings rather than
occurring naturally, especially as a copy of
something natural.
However, artificiality does not necessarily have a
negative connotation, as it may also reflect the
ability of humans to replicate forms or functions
arising in nature, as with an artificial heart or
artificial intelligence.
Intelligence expert Herbert A. Simon observes
that "some artificial things are imitations of
things in nature, and the imitation may use
either the same basic materials as those in the
natural object or quite different materials.
3ANN by Gagan Deep, rozygag@yahoo.com
4. Artificial Intelligence
Artificial intelligence (AI) is the intelligence
exhibited by machines or software. It is an
academic field of study which studies the
goal of creating intelligence.
The central problems (or goals) of AI research
include reasoning, knowledge, planning,
learning, natural language processing
(communication), perception and the ability
to move and manipulate objects.
4ANN by Gagan Deep, rozygag@yahoo.com
5. Knowledge Based System
Knowledge-based system is a program that
acquires, represents and uses knowledge for
a specific purpose.
Consists of a knowledge-base and an
inference engine.
Knowledge is stored in the knowledge-base
while control strategies reside in the separate
inference engine.
5ANN by Gagan Deep, rozygag@yahoo.com
7. Stages of Biological Neural System
The neural system of the human body consists of three
stages: receptors, a neural network, and effectors. The
receptors receive the stimuli either internally or from the
external world, then pass the information into the neurons in
a form of electrical impulses. The neural network then
processes the inputs then makes proper decision of outputs.
Finally, the effectors translate electrical impulses from the
neural network into responses to the outside environment.
Figure shows the bidirectional communication between
stages for feedback
7ANN by Gagan Deep, rozygag@yahoo.com
8. Neural
Neural relating to a nerve or the nervous
system.
Situated in the region of or on the same side
of the body as the brain and spinal cord.
It came from the Greek word Neuron.
8ANN by Gagan Deep, rozygag@yahoo.com
9. Neuron
A neuron also known as a neurone or nerve
cell) is an electrically excitable cell that
processes and transmits information through
electrical and chemical signals.
These signals between neurons occur via
synapses, specialized connections with other
cells.
Synapses - a junction between two nerve
cells, consisting of a minute gap across which
impulses pass by diffusion of a
neurotransmitter.
9ANN by Gagan Deep, rozygag@yahoo.com
10. The human body is made up of trillions of cells.
Neurons, are specialized to carry "messages"
through an electrochemical process.
The human brain has approximately 100 billion
neurons.
Neurons come in many different shapes and
sizes.
Some of the smallest neurons have cell bodies
that are only 4 microns wide.
Some of the biggest neurons have cell bodies
that are 100 microns wide. (Remember that 1
micron is equal to one thousandth of a
millimeter!).
10ANN by Gagan Deep, rozygag@yahoo.com
11. Neurons vs. Other Cells
Similarities with other cells:
Neurons are surrounded by a cell membrane
that protects the cell.
Neurons and other body cells both contain a
nucleus that holds genetic information.
Neurons carry out basic cellular processes
such as protein synthesis and energy
production.
11ANN by Gagan Deep, rozygag@yahoo.com
12. However, neurons differ from other cells in the
body because:
Neurons have specialize cell parts called
dendrites and axons. Dendrites bring
electrical signals to the cell body and axons
take information away from the cell body.
Neurons communicate with each other
through an electrochemical process.
Neurons contain some specialized structures
(for example, synapses) and chemicals (for
example, neurotransmitters).
12ANN by Gagan Deep, rozygag@yahoo.com
13. The Structure of a Neuron
There are three basic parts of a neuron: the
dendrites, the cell body and the axon.
However, all neurons vary somewhat in size,
shape, and characteristics depending on the
function and role of the neuron.
Some neurons have few dendritic branches,
while others are highly branched in order to
receive a great deal of information.
Some neurons have short axons, while others
can be quite long. The longest axon in the human
body extends from the bottom of the spine to
the big toe and averages a length of
approximately three feet!
13ANN by Gagan Deep, rozygag@yahoo.com
14. Neuron
One way to classify neurons is by the number of
extensions that extend from the neuron's cell body
(soma).
14ANN by Gagan Deep, rozygag@yahoo.com
16. Bipolar neurons have two processes extending from the cell body (examples:
retinal cells, olfactory epithelium cells).
Pseudounipolar cells (example: dorsal root ganglion cells).Actually, these cells
have 2 axons rather than an axon and dendrite. One axon extends centrally
toward the spinal cord, the other axon extends toward the skin or muscle.
Multipolar neurons have many processes that extend from the cell body.
However, each neuron has only one axon (examples: spinal motor neurons,
pyramidal neurons, Purkinje cells). 16ANN by Gagan Deep, rozygag@yahoo.com
20. Neurons can also be classified by the direction
that they send information.
Sensory (or afferent) neurons: send
information from sensory receptors (e.g., in
skin, eyes, nose, tongue, ears) TOWARD the
central nervous system.
Motor (or efferent) neurons: send
information AWAY from the central nervous
system to muscles or glands.
Interneuron: send information between
sensory neurons and motor neurons. Most
interneuron's are located in the central
nervous system.
20ANN by Gagan Deep, rozygag@yahoo.com
21. Action Potentials
How do neurons transmit and receive
information? In order for neurons to
communicate, they need to transmit information
both within the neuron and from one neuron to
the next. This process utilizes both electrical
signals as well as chemical messengers.
The dendrites of neurons receive information
from sensory receptors or other neurons. This
information is then passed down to the cell body
and on to the axon. Once the information as
arrived at the axon, it travels down the length of
the axon in the form of an electrical signal known
as an action potential.
21ANN by Gagan Deep, rozygag@yahoo.com
22. Communication Between Synapses
Once an electrical impulse has reached the end of an
axon, the information must be transmitted across
the synaptic gap to the dendrites of the adjoining
neuron. In some cases, the electrical signal can
almost instantaneously bridge the gap between the
neurons and continue along its path.
In other cases, neurotransmitters are needed to send
the information from one neuron to the next.
Neurotransmitters are chemical messengers that are
released from the axon terminals to cross the
synaptic gap and reach the receptor sites of other
neurons. In a process known as reuptake, these
neurotransmitters attach to the receptor site and
are reabsorbed by the neuron to be reused.
22ANN by Gagan Deep, rozygag@yahoo.com
23. Neurotransmitters
Neurotransmitters are an essential part of our
everyday functioning. While it is not known
exactly how many neurotransmitters exist,
scientists have identified more than 100 of these
chemical messengers.
The spikes travelling along the axon of the pre-
synaptic neuron trigger the release of
neurotransmitter substances at the synapse.
The neurotransmitters cause excitation or
inhibition in the dendrite of the post-synaptic
neuron.
23ANN by Gagan Deep, rozygag@yahoo.com
24. The integration of the excitatory and
inhibitory signals may produce spikes in the
post-synaptic neuron.
The contribution of the signals depends on
the strength of the synaptic connection.
What effects do each of these
neurotransmitters have on the body?
What happens when disease or drugs
interfere with these chemical messengers?
The following are just a few of the major
neurotransmitters, their known effects, and
disorders they are associated with.
24ANN by Gagan Deep, rozygag@yahoo.com
25. Acetylcholine: Associated with memory, muscle
contractions, and learning. A lack of
acetylcholine in the brain is associated with
Alzheimer’s disease.
Endorphins: Associated with emotions and pain
perception. The body releases endorphins in
response to fear or trauma. These chemical
messengers are similar to opiate drugs such as
morphine, but are significantly stronger.
Dopamine: Associated with thought and
pleasurable feelings. Parkinson’s disease is one
illness associated with deficits in dopamine,
while schizophrenia is strongly linked to
excessive amounts of this chemical messenger.
25ANN by Gagan Deep, rozygag@yahoo.com
26. Biological Prototype
● Neuron
- Information gathering (D)
- Information processing (C)
- Information propagation (A / S)
human being: 1012 neurons
electricity in mV range
speed: 120 m / s
cell body (C)
dendrite (D)nucleus
axon (A)
synapse (S)
26ANN by Gagan Deep, rozygag@yahoo.com
27. Artificial Neural Network
An Artificial Neural Network (ANN) is an
information processing paradigm that is inspired by
the way biological nervous systems, such as the
brain, process information.T
The key element of this paradigm is the novel
structure of the information processing system.
It is composed of a large number of highly
interconnected processing elements (neurones)
working in unison to solve specific problems.
ANNs, like people, learn by example.
An ANN is configured for a specific application, such
as pattern recognition or data classification,
through a learning process.
27ANN by Gagan Deep, rozygag@yahoo.com
28. Learning in biological systems involves adjustments
to the synaptic connections that exist between the
neurones.This is true of ANNs as well.
28ANN by Gagan Deep, rozygag@yahoo.com
29. BRAIN COMPUTATION
The human brain contains about 10
billion nerve cells, or neurons. On
average, each neuron is connected to
other neurons through approximately
10,000 synapses.
29ANN by Gagan Deep, rozygag@yahoo.com
30. DEFINITION OF NEURAL NETWORKS
According to the DARPA Neural Network Study
• ... a neural network is a system composed of many
simple processing elements operating in parallel whose
function is determined by network structure, connection
strengths, and the processing performed at computing
elements or nodes.
According to Haykin
A neural network is a massively parallel distributed
processor that has a natural propensity for storing
experiential knowledge and making it available for use. It
resembles the brain in two respects:
• Knowledge is acquired by the network through a learning process.
• Interneuron connection strengths known as synaptic weights are
used to store the knowledge.
30ANN by Gagan Deep, rozygag@yahoo.com
31. NEURAL NETWORKS v/s CONVENTIONAL COMPUTERS
COMPUTERS
Algorithmic approach
They are necessarily
programmed
Work on predefined
set of instructions
Operations are
predictable
ANN
Learning approach
Not programmed for
specific tasks
Used in decision
making
Operation is
unpredictable
31ANN by Gagan Deep, rozygag@yahoo.com
32. ARTIFICIAL NEURAL NETWORKS
Information-processing system.
Neurons process the information.
The signals are transmitted by means of
connection links.
The links possess an associated weight.
The output signal is obtained by applying
activations to the net input.
32ANN by Gagan Deep, rozygag@yahoo.com
33. ARTIFICIAL NEURAL NETWORKS
The figure shows a simple artificial neural net
with two input neurons (X1, X2) and one
output neuron (Y). The inter connected
weights are given byW1 and W2.
X2
X1
W2
W1
Y
33ANN by Gagan Deep, rozygag@yahoo.com
35. PROCESSING OF AN ARTIFICIAL NETWORKS
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.
j
j
jXWu
m
1
)(uy b
35ANN by Gagan Deep, rozygag@yahoo.com
36. MOTIVATION FOR NEURAL NET
Scientists are challenged to use machines more
effectively for tasks currently solved by humans.
Symbolic rules don't reflect processes actually used
by humans.
Traditional computing excels in many areas, but not
in others.
36ANN by Gagan Deep, rozygag@yahoo.com
37. The major areas being:
Massive parallelism
Distributed representation and computation
Learning ability
Generalization ability
Adaptivity
Inherent contextual information processing
Fault tolerance
Low energy consumption
37ANN by Gagan Deep, rozygag@yahoo.com
38. Characteristics of Artificial Neural Networks
A large number of very simple processing
neuron-like processing elements
A large number of weighted connections
between the elements
Distributed representation of knowledge over
the connections
Knowledge is acquired by network through a
learning process
38ANN by Gagan Deep, rozygag@yahoo.com
39. The good news: They exhibit some brain-like
behaviors that are difficult to program
directly like:
learning
association
categorization
generalization
feature extraction
optimization
noise immunity
The bad news: neural nets are
black boxes
difficult to train in some cases
39ANN by Gagan Deep, rozygag@yahoo.com
40. The NN exhibit mapping capabilities that is they can
input patterns to their associated output patterns.
The NN learn by example. Thus, NN architecture can
be trained with known examples of a problem before
they are tested for their ‘inference’ capability on
unknown instances of the problem. They can,
therefore identify new objects previously untrained.
The NN possess the capability to generalize. Thus,
they can predict new outcomes from past trends.
The NNs are robust systems and are fault tolerant.
They can, therefore, recall full patterns from
incomplete, partial or noisy patterns.
The NNs can process information in parallel, at high
speed, and in a distributed manner.
40ANN by Gagan Deep, rozygag@yahoo.com
41. Features of Biological Neural Networks
Some attractive features of the biological NN that
make it superior to even the most sophisticated AI
computer system pattern recognition tasks are the
following
Robustness and fault tolerance : The decay of nerve
cells does not seem to affect the performance
significantly.
Flexibility : The network automatically adjust to a
new environment without using any programmed
instruction.
Ability to deal with a variety of data situations : The
network can deal with information that is fuzzy,
probabilistics, noisy and inconsistent.
Collective computation : The network performs
routinely many operations in parrallel and also given
task in a distributed manner.
41ANN by Gagan Deep, rozygag@yahoo.com
42. Performance Comparison of Computer and Biological
Neural Networks
Speed: Brain (Slow in processing information) +
Computer (Fast)= ANN (Fast)
Processing :Sequential (Programs) & Parallel (Brain)=
Parallel Processing
Size & Complexity : Billion & trillions of neurons and
their interconnections- so they give size and
complexity.
Storage : Brain (Adaptable) /Computer (Strictly
Replaceable)- In computers overwriting takes place but
in brain according to interconnection strengths they
add.
Fault Tolerance : Due to Distributed Networks
information can be retrieved after any
crash/destruction
Control Mechanism : Central nervous system (Brain)/
Computer (Control Unit)
42ANN by Gagan Deep, rozygag@yahoo.com
43. HISTORICAL BACKGROUND
The history of neural networks that was described
above can be divided into several periods:
First Attempts: There were some initial simulations
using formal logic. McCulloch and Pitts (1943)
developed models of neural networks based on their
understanding of neurology. These models made
several assumptions about how neurons worked. Their
networks were based on simple neurons which were
considered to be binary devices with fixed thresholds.
The results of their model were simple logic functions
such as "a or b" and "a and b".
43ANN by Gagan Deep, rozygag@yahoo.com
44. Another attempt was by using computer
simulations. Two groups (Farley and Clark, 1954;
Rochester, Holland, Haibit and Duda, 1956). The
first group (IBM researchers) maintained closed
contact with neuroscientists at McGill University.
So whenever their models did not work, they
consulted the neuroscientists. This interaction
established a multidisciplinary trend which
continues to the present day.
44ANN by Gagan Deep, rozygag@yahoo.com
45. Promising & Emerging Technology: Not
only was neuroscience influential in the
development of neural networks, but
psychologists and engineers also contributed
to the progress of neural network
simulations.
Rosenblatt (1958) stirred considerable
interest and activity in the field when he
designed and developed the Perceptron. The
Perceptron had three layers with the middle
layer known as the association layer. This
system could learn to connect or associate a
given input to a random output unit.
45ANN by Gagan Deep, rozygag@yahoo.com
46. Another system was the ADALINE (ADAptive
LInear Element) which was developed in 1960
by Widrow and Hoff (of Stanford University).
The ADALINE was an analogue electronic
device made from simple components. The
method used for learning was different to
that of the Perceptron, it employed the
Least-Mean-Squares (LMS) learning rule.
46ANN by Gagan Deep, rozygag@yahoo.com
47. Period of Frustration & Disrepute: In 1969
Minsky and Papert wrote a book in which
they generalized the limitations of single
layer Perceptrons to multilayered systems. In
the book they said:
"...our intuitive judgment that the extension
(to multilayer systems) is sterile".
The significant result of their book was to
eliminate funding for research with neural
network simulations. The conclusions
supported the disenchantment of researchers
in the field. As a result, considerable prejudice
against this field was activated.
47ANN by Gagan Deep, rozygag@yahoo.com
48. Innovation: Although public interest and available
funding were minimal, several researchers
continued working to develop neuromorphically
based computational methods for problems such as
pattern recognition.
During this period several paradigms were
generated which modern work continues to
enhance. Grossberg's (Steve Grossberg and Gail
Carpenter in 1988) influence founded a school of
thought which explores resonating algorithms. They
developed the ART (Adaptive Resonance Theory)
networks based on biologically plausible models.
Anderson and Kohonen developed associative
techniques independent of each other. Klopf (A.
Henry Klopf) in 1972, developed a basis for learning
in artificial neurons based on a biological principle
for neuronal learning called heterostasis.
48ANN by Gagan Deep, rozygag@yahoo.com
49. Werbos (Paul Werbos 1974) developed and used the
back-propagation learning method, however several
years passed before this approach was popularized.
Back-propagation nets are probably the most well
known and widely applied of the neural networks
today. In essence, the back-propagation net. is a
Perceptron with multiple layers, a different threshold
function in the artificial neuron, and a more robust
and capable learning rule.
Amari (A. Shun-Ichi 1967) was involved with
theoretical developments: he published a paper
which established a mathematical theory for a
learning basis (error-correction method) dealing with
adaptive pattern classification. While Fukushima (F.
Kunihiko) developed a step wise trained multilayered
neural network for interpretation of handwritten
characters. The original network was published in
1975 and was called the Cognitron.
49ANN by Gagan Deep, rozygag@yahoo.com
50. Re-Emergence: Progress during the late 1970s and
early 1980s was important to the re-emergence on
interest in the neural network field. Several factors
influenced this movement.
For example, comprehensive books and conferences
provided a forum for people in diverse fields with
specialized technical languages, and the response to
conferences and publications was quite positive. The
news media picked up on the increased activity and
tutorials helped disseminate the technology.
Academic programs appeared and courses were
introduced at most major Universities (in US and
Europe). Attention is now focused on funding levels
throughout Europe, Japan and the US and as this
funding becomes available, several new commercial
with applications in industry and financial
institutions are emerging.
.
50ANN by Gagan Deep, rozygag@yahoo.com
51. Today: Significant progress has been made in
the fild of neural networks-enough to attract
a great deal of attention and fund further
research.
Advancement beyond current commercial
applications appears to be possible, and
research is advancing the field on many
fronts.
Neurally based chips are emerging and
applications to complex problems
developing.
Clearly, today is a period of transition for
neural network technology
51ANN by Gagan Deep, rozygag@yahoo.com
53. We Discussed(marked)
Unit I
Introduction: Concepts of neural networks,
Characteristics of Neural Networks, Historical
Perspective, and Applications of Neural
Networks.
Fundamentals of Neural Networks: The
biological prototype, Neuron concept, Single
layer Neural Networks, Multi-Layer Neural
Networks, terminology, Notation and
representation of Neural Networks, Training of
Artificial Neural Networks.
Representation of perceptron and issues,
perceptron learning and training, Classification,
linear Separability
53ANN by Gagan Deep, rozygag@yahoo.com