The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
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.
hetero associative memory is a single layer neural network. However, in this network the input training vector and the output target vectors are not the same. The weights are determined so that the network stores a set of patterns. Hetero associative network is static in nature, hence, there would be no non-linear and delay operations.
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.
This document discusses gradient descent algorithms, feedforward neural networks, and backpropagation. It defines machine learning, artificial intelligence, and deep learning. It then explains gradient descent as an optimization technique used to minimize cost functions in deep learning models. It describes feedforward neural networks as having connections that move in one direction from input to output nodes. Backpropagation is mentioned as an algorithm for training neural networks.
The document provides an overview of perceptrons and neural networks. It discusses how neural networks are modeled after the human brain and consist of interconnected artificial neurons. The key aspects covered include the McCulloch-Pitts neuron model, Rosenblatt's perceptron, different types of learning (supervised, unsupervised, reinforcement), the backpropagation algorithm, and applications of neural networks such as pattern recognition and machine translation.
The document discusses various neural network learning rules:
1. Error correction learning rule (delta rule) adapts weights based on the error between the actual and desired output.
2. Memory-based learning stores all training examples and classifies new inputs based on similarity to nearby examples (e.g. k-nearest neighbors).
3. Hebbian learning increases weights of simultaneously active neuron connections and decreases others, allowing patterns to emerge from correlations in inputs over time.
4. Competitive learning (winner-take-all) adapts the weights of the neuron most active for a given input, allowing unsupervised clustering of similar inputs across neurons.
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.
hetero associative memory is a single layer neural network. However, in this network the input training vector and the output target vectors are not the same. The weights are determined so that the network stores a set of patterns. Hetero associative network is static in nature, hence, there would be no non-linear and delay operations.
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.
This document discusses gradient descent algorithms, feedforward neural networks, and backpropagation. It defines machine learning, artificial intelligence, and deep learning. It then explains gradient descent as an optimization technique used to minimize cost functions in deep learning models. It describes feedforward neural networks as having connections that move in one direction from input to output nodes. Backpropagation is mentioned as an algorithm for training neural networks.
This document describes the Hebbian learning rule, a single-layer neural network algorithm. The Hebbian rule updates weights between neurons based on their activation. Given an input, the output neuron's activation and the target output are used to update the weights according to the rule wi new = wi old + xiy. The document provides an example of using the Hebbian rule to train a network to perform the AND logic function over four training iterations. Over the iterations, the weights adjust until the network correctly classifies all four input patterns.
Introduction Of Artificial neural networkNagarajan
The document summarizes different types of artificial neural networks including their structure, learning paradigms, and learning rules. It discusses artificial neural networks (ANN), their advantages, and major learning paradigms - supervised, unsupervised, and reinforcement learning. It also explains different mathematical synaptic modification rules like backpropagation of error, correlative Hebbian, and temporally-asymmetric Hebbian learning rules. Specific learning rules discussed include the delta rule, the pattern associator, and the Hebb rule.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
The document provides an introduction to artificial neural networks and their components. It discusses the basic neuron model, including the summation function, activation function, and bias. It also covers various neuron models based on different activation functions. The document introduces different network architectures, including single-layer feedforward networks, multilayer feedforward networks, and recurrent networks. It discusses perceptrons, ADALINE networks, and the backpropagation algorithm for training multilayer networks. The limitations of perceptrons for non-linearly separable problems are also covered.
The document summarizes the counterpropagation neural network algorithm. It consists of an input layer, a Kohonen hidden layer that clusters inputs, and a Grossberg output layer. The algorithm identifies the winning hidden neuron that is most activated by the input. The output is then calculated as the weight between the winning hidden neuron and the output neurons, providing a coarse approximation of the input-output mapping.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Here is a MATLAB program to implement logic functions using a McCulloch-Pitts neuron:
% McCulloch-Pitts neuron for logic functions
% Inputs
x1 = 1;
x2 = 0;
% Weights
w1 = 1;
w2 = 1;
% Threshold
theta = 2;
% Net input
net = x1*w1 + x2*w2;
% Activation function
if net >= theta
y = 1;
else
y = 0;
end
% Output
disp(y)
This implements a basic AND logic gate using a McCulloch-Pitts neuron.
The document discusses the Least-Mean Square (LMS) algorithm. It begins by introducing LMS as the first linear adaptive filtering algorithm developed by Widrow and Hoff in 1960. It then describes the filtering structure of LMS, modeling an unknown dynamic system using a linear neuron model and adjusting weights based on an error signal. Finally, it summarizes the LMS algorithm, outlines its virtues like computational simplicity and robustness, and notes its primary limitation is slow convergence for high-dimensional problems.
Artificial neural networks mimic the human brain by using interconnected layers of neurons that fire electrical signals between each other. Activation functions are important for neural networks to learn complex patterns by introducing non-linearity. Without activation functions, neural networks would be limited to linear regression. Common activation functions include sigmoid, tanh, ReLU, and LeakyReLU, with ReLU and LeakyReLU helping to address issues like vanishing gradients that can occur with sigmoid and tanh functions.
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
The document discusses Adaline and Madaline artificial neural networks. It provides information on:
- Adaline networks, which are simple perceptrons that accomplish classification by modifying weights to minimize mean square error. Adaline uses the Widrow-Hoff learning rule.
- Madaline networks, which combine multiple Adalines and can solve non-separable problems. Madaline rule training algorithms include Madaline Rule I, II, and III.
- Madaline Rule I modifies weights leading into hidden nodes to decrease error on each input. Madaline Rule II modifies weights layer-by-layer using a trial-and-error approach.
- Applications of Adaline include noise cancellation, echo cancellation, and medical
Principles of soft computing-Associative memory networksSivagowry Shathesh
The document discusses various types of associative memory networks including auto-associative, hetero-associative, bidirectional associative memory (BAM), and Hopfield networks. It describes the architecture, training algorithms, and testing procedures for each type of network. The key points are: Auto-associative networks store and recall patterns using the same input and output vectors, while hetero-associative networks use different input and output vectors. BAM networks perform bidirectional retrieval of patterns. Hopfield networks are auto-associative single-layer recurrent networks that can converge to stable states representing stored patterns. Hebbian learning and energy functions are important concepts in analyzing the storage and recall capabilities of these associative memory networks.
This document discusses kernel methods and radial basis function (RBF) networks. It begins with an introduction and overview of Cover's theory of separability of patterns. It then revisits the XOR problem and shows how it can be solved using Gaussian hidden functions. The interpolation problem is explained and how RBF networks can perform strict interpolation through a set of training data points. Radial basis functions that satisfy Micchelli's theorem allowing for a nonsingular interpolation matrix are presented. Finally, the structure and training of RBF networks using k-means clustering and recursive least squares estimation is covered.
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
The document discusses analytical learning methods like explanation-based learning. It explains that analytical learning uses prior knowledge and deductive reasoning to augment training examples, allowing it to generalize better than methods relying solely on data. Explanation-based learning analyzes examples according to prior knowledge to infer relevant features. The document provides examples of using explanation-based learning to learn chess concepts and safe stacking of objects. It also describes the PROLOG-EBG algorithm for explanation-based learning.
This document discusses problem solving agents in artificial intelligence. It explains that problem solving agents focus on satisfying goals by formulating the goal based on the current situation, then formulating the problem by determining the actions needed to achieve the goal. Key components of problem formulation include the initial state, possible actions, transition model describing how actions change the state, a goal test, and path cost function. Two examples of well-defined problems are given: the 8-puzzle problem and the 8-queens problem.
This document describes the Hebbian learning rule, a single-layer neural network algorithm. The Hebbian rule updates weights between neurons based on their activation. Given an input, the output neuron's activation and the target output are used to update the weights according to the rule wi new = wi old + xiy. The document provides an example of using the Hebbian rule to train a network to perform the AND logic function over four training iterations. Over the iterations, the weights adjust until the network correctly classifies all four input patterns.
Introduction Of Artificial neural networkNagarajan
The document summarizes different types of artificial neural networks including their structure, learning paradigms, and learning rules. It discusses artificial neural networks (ANN), their advantages, and major learning paradigms - supervised, unsupervised, and reinforcement learning. It also explains different mathematical synaptic modification rules like backpropagation of error, correlative Hebbian, and temporally-asymmetric Hebbian learning rules. Specific learning rules discussed include the delta rule, the pattern associator, and the Hebb rule.
The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.
The document discusses planning and problem solving in artificial intelligence. It describes planning problems as finding a sequence of actions to achieve a given goal state from an initial state. Common assumptions in planning include atomic time steps, deterministic actions, and a closed world. Blocks world examples are provided to illustrate planning domains and representations using states, goals, and operators. Classical planning approaches like STRIPS are summarized.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
The document provides an introduction to artificial neural networks and their components. It discusses the basic neuron model, including the summation function, activation function, and bias. It also covers various neuron models based on different activation functions. The document introduces different network architectures, including single-layer feedforward networks, multilayer feedforward networks, and recurrent networks. It discusses perceptrons, ADALINE networks, and the backpropagation algorithm for training multilayer networks. The limitations of perceptrons for non-linearly separable problems are also covered.
The document summarizes the counterpropagation neural network algorithm. It consists of an input layer, a Kohonen hidden layer that clusters inputs, and a Grossberg output layer. The algorithm identifies the winning hidden neuron that is most activated by the input. The output is then calculated as the weight between the winning hidden neuron and the output neurons, providing a coarse approximation of the input-output mapping.
In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.
Here is a MATLAB program to implement logic functions using a McCulloch-Pitts neuron:
% McCulloch-Pitts neuron for logic functions
% Inputs
x1 = 1;
x2 = 0;
% Weights
w1 = 1;
w2 = 1;
% Threshold
theta = 2;
% Net input
net = x1*w1 + x2*w2;
% Activation function
if net >= theta
y = 1;
else
y = 0;
end
% Output
disp(y)
This implements a basic AND logic gate using a McCulloch-Pitts neuron.
The document discusses the Least-Mean Square (LMS) algorithm. It begins by introducing LMS as the first linear adaptive filtering algorithm developed by Widrow and Hoff in 1960. It then describes the filtering structure of LMS, modeling an unknown dynamic system using a linear neuron model and adjusting weights based on an error signal. Finally, it summarizes the LMS algorithm, outlines its virtues like computational simplicity and robustness, and notes its primary limitation is slow convergence for high-dimensional problems.
Artificial neural networks mimic the human brain by using interconnected layers of neurons that fire electrical signals between each other. Activation functions are important for neural networks to learn complex patterns by introducing non-linearity. Without activation functions, neural networks would be limited to linear regression. Common activation functions include sigmoid, tanh, ReLU, and LeakyReLU, with ReLU and LeakyReLU helping to address issues like vanishing gradients that can occur with sigmoid and tanh functions.
Radial basis function network ppt bySheetal,Samreen and Dhanashrisheetal katkar
Radial Basis Functions are nonlinear activation functions used by artificial neural networks.Explained commonly used RBFs ,cover's theorem,interpolation problem and learning strategies.
The document discusses Adaline and Madaline artificial neural networks. It provides information on:
- Adaline networks, which are simple perceptrons that accomplish classification by modifying weights to minimize mean square error. Adaline uses the Widrow-Hoff learning rule.
- Madaline networks, which combine multiple Adalines and can solve non-separable problems. Madaline rule training algorithms include Madaline Rule I, II, and III.
- Madaline Rule I modifies weights leading into hidden nodes to decrease error on each input. Madaline Rule II modifies weights layer-by-layer using a trial-and-error approach.
- Applications of Adaline include noise cancellation, echo cancellation, and medical
Principles of soft computing-Associative memory networksSivagowry Shathesh
The document discusses various types of associative memory networks including auto-associative, hetero-associative, bidirectional associative memory (BAM), and Hopfield networks. It describes the architecture, training algorithms, and testing procedures for each type of network. The key points are: Auto-associative networks store and recall patterns using the same input and output vectors, while hetero-associative networks use different input and output vectors. BAM networks perform bidirectional retrieval of patterns. Hopfield networks are auto-associative single-layer recurrent networks that can converge to stable states representing stored patterns. Hebbian learning and energy functions are important concepts in analyzing the storage and recall capabilities of these associative memory networks.
This document discusses kernel methods and radial basis function (RBF) networks. It begins with an introduction and overview of Cover's theory of separability of patterns. It then revisits the XOR problem and shows how it can be solved using Gaussian hidden functions. The interpolation problem is explained and how RBF networks can perform strict interpolation through a set of training data points. Radial basis functions that satisfy Micchelli's theorem allowing for a nonsingular interpolation matrix are presented. Finally, the structure and training of RBF networks using k-means clustering and recursive least squares estimation is covered.
Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.
The document discusses analytical learning methods like explanation-based learning. It explains that analytical learning uses prior knowledge and deductive reasoning to augment training examples, allowing it to generalize better than methods relying solely on data. Explanation-based learning analyzes examples according to prior knowledge to infer relevant features. The document provides examples of using explanation-based learning to learn chess concepts and safe stacking of objects. It also describes the PROLOG-EBG algorithm for explanation-based learning.
This document discusses problem solving agents in artificial intelligence. It explains that problem solving agents focus on satisfying goals by formulating the goal based on the current situation, then formulating the problem by determining the actions needed to achieve the goal. Key components of problem formulation include the initial state, possible actions, transition model describing how actions change the state, a goal test, and path cost function. Two examples of well-defined problems are given: the 8-puzzle problem and the 8-queens problem.
The document discusses artificial neural networks (ANNs) and summarizes key information about soft computing techniques, ANNs, and some specific ANN models including perceptrons, ADALINE, and MADALINE. It defines soft computing as a collection of computational techniques including neural networks, fuzzy logic, and evolutionary computing. ANNs are modeled after the human brain and consist of interconnected neurons that can learn from examples. Perceptrons, ADALINE, and MADALINE are early ANN models that use different learning rules to update weights and biases.
The document discusses artificial neural networks (ANNs) and summarizes key information about ANNs and related topics. It defines soft computing as a field that aims to build intelligent machines using techniques like ANNs, fuzzy logic, and evolutionary computing. ANNs are modeled after biological neural networks and consist of interconnected nodes that can learn from data. Early ANN models like the perceptron, ADALINE, and MADALINE are described along with their learning rules and architectures. Applications of ANNs in various domains are also listed.
The document discusses soft computing and artificial neural networks. It provides an overview of soft computing techniques including artificial neural networks (ANNs), fuzzy logic, and evolutionary computing. It then focuses on ANNs, describing their biological inspiration from neurons in the brain. The basic components of ANNs are discussed including network architecture, learning algorithms, and activation functions. Specific ANN models are then summarized, such as the perceptron, ADALINE, and their learning rules. Applications of ANNs are also briefly mentioned.
This document provides instructions for three exercises using artificial neural networks (ANNs) in Matlab: function fitting, pattern recognition, and clustering. It begins with background on ANNs including their structure, learning rules, training process, and common architectures. The exercises then guide using ANNs in Matlab for regression to predict house prices from data, classification of tumors as benign or malignant, and clustering of data. Instructions include loading data, creating and training networks, and evaluating results using both the GUI and command line. Improving results through retraining or adding neurons is also discussed.
This presentation discusses the following ANN concepts:
Introduction
Characteristics
Learning methods
Taxonomy
Evolution of neural networks
Basic models
Important technologies
Applications
This document provides an overview and literature review of unsupervised feature learning techniques. It begins with background on machine learning and the challenges of feature engineering. It then discusses unsupervised feature learning as a framework to learn representations from unlabeled data. The document specifically examines sparse autoencoders, PCA, whitening, and self-taught learning. It provides details on the mathematical concepts and implementations of these algorithms, including applying them to learn features from images. The goal is to use unsupervised learning to extract features that can enhance supervised models without requiring labeled training data.
Survey on Artificial Neural Network Learning Technique AlgorithmsIRJET Journal
This document discusses different types of learning algorithms used in artificial neural networks. It begins with an introduction to neural networks and their ability to learn from their environment through adjustments to synaptic weights. Four main learning algorithms are then described: error correction learning, which uses algorithms like backpropagation to minimize error; memory based learning, which stores all training examples and analyzes nearby examples to classify new inputs; Hebbian learning, where connection weights are adjusted based on the activity of neurons; and competitive learning, where neurons compete to respond to inputs to become specialized feature detectors through a winner-take-all mechanism. The document provides details on how each type of learning algorithm works.
This document discusses neural networks and multilayer feedforward neural network architectures. It describes how multilayer networks can solve nonlinear classification problems using hidden layers. The backpropagation algorithm is introduced as a way to train these networks by propagating error backwards from the output to adjust weights. The architecture of a neural network is explained, including input, hidden, and output nodes. Backpropagation is then described in more detail through its training process of forward passing input, calculating error at the output, and propagating this error backwards to update weights. Examples of backpropagation and its applications are also provided.
This document discusses how machines can make decisions using machine learning approaches. It provides an overview of machine learning vocabulary and techniques including supervised learning methods like regression and classification. It also discusses unsupervised learning and examples of clustering emails. The document then demonstrates simple linear and logistic regression models to predict outputs for given inputs. It discusses evaluating models through error measurement and mentions some other machine learning techniques. Finally, it provides an overview of neural networks including feedforward networks and different types like convolutional and recurrent neural networks.
This document discusses how machines can make decisions using machine learning approaches. It provides an overview of machine learning vocabulary and techniques including supervised learning methods like regression and classification. It also discusses unsupervised learning and examples of clustering emails. The document then demonstrates simple linear and logistic regression models to predict outputs given inputs. It discusses evaluating models through error measurement and mentions several other machine learning techniques. Finally, it provides an overview of neural networks including feedforward networks and different types like convolutional and recurrent neural networks.
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
The document compares the performance of an autoassociative memory with and without using a pseudoinverse weight matrix. It finds that using the pseudoinverse weight matrix improves performance in both noise-free conditions and when noise is present. Specifically, it finds that without the pseudoinverse, the weight matrix has a larger range of values and more cross-correlation, resulting in more character errors. With the pseudoinverse, the weight matrix range is limited to 0 to 1, improving performance both without and with noise. The autoassociative memory using the pseudoinverse weight matrix thus demonstrates much better performance.
This document provides an overview of running an image classification workload using IBM PowerAI and the MNIST dataset. It discusses deep learning concepts like neural networks and training flows. It then demonstrates how to set up TensorFlow on an IBM PowerAI trial server, load the MNIST dataset, build and train a basic neural network model for image classification, and evaluate the trained model's accuracy on test data.
The document presents a project on sentiment analysis of human emotions, specifically focusing on detecting emotions from babies' facial expressions using deep learning. It involves loading a facial expression dataset, training a convolutional neural network model to classify 7 emotions (anger, disgust, fear, happy, sad, surprise, neutral), and evaluating the model on test data. An emotion detection application is implemented using the trained model to analyze emotions in real-time images from a webcam with around 60-70% accuracy on random images.
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 presentation discusses the following topics:
Basic features of R
Exploring R GUI
Data Frames & Lists
Handling Data in R Workspace
Reading Data Sets & Exporting Data from R
Manipulating & Processing Data in R
Association rule mining is used to find relationships between items in transaction data. It identifies rules that can predict the occurrence of an item based on other items purchased together frequently. Some key metrics used to evaluate rules include support, which measures how frequently an itemset occurs; confidence, which measures how often items in the predicted set occur given items in the predictor set; and lift, which compares the confidence to expected confidence if items were independent. An example association rule evaluated is {Milk, Diaper} -> {Beer} with support of 0.4, confidence of 0.67, and lift of 1.11.
This document discusses clustering, which is the task of grouping data points into clusters so that points within the same cluster are more similar to each other than points in other clusters. It describes different types of clustering methods, including density-based, hierarchical, partitioning, and grid-based methods. It provides examples of specific clustering algorithms like K-means, DBSCAN, and discusses applications of clustering in fields like marketing, biology, libraries, insurance, city planning, and earthquake studies.
Classification is a data analysis technique used to predict class membership for new observations based on a training set of previously labeled examples. It involves building a classification model during a training phase using an algorithm, then testing the model on new data to estimate accuracy. Some common classification algorithms include decision trees, Bayesian networks, neural networks, and support vector machines. Classification has applications in domains like medicine, retail, and entertainment.
The document discusses the assumptions and properties of ordinary least squares (OLS) estimators in linear regression analysis. It notes that OLS estimators are best linear unbiased estimators (BLUE) if the assumptions of the linear regression model are met. Specifically, it assumes errors have zero mean and constant variance, are uncorrelated, and are normally distributed. Violation of the assumption of constant variance is known as heteroscedasticity. The document outlines how heteroscedasticity impacts the properties of OLS estimators and their use in applications like econometrics.
This document provides an introduction to regression analysis. It discusses that regression analysis investigates the relationship between dependent and independent variables to model and analyze data. The document outlines different types of regressions including linear, polynomial, stepwise, ridge, lasso, and elastic net regressions. It explains that regression analysis is used for predictive modeling, forecasting, and determining the impact of variables. The benefits of regression analysis are that it indicates significant relationships and the strength of impact between variables.
MYCIN was an early expert system developed at Stanford University in 1972 to assist physicians in diagnosing and selecting treatment for bacterial and blood infections. It used over 600 production rules encoding the clinical decision criteria of infectious disease experts to diagnose patients based on reported symptoms and test results. While it could not replace human diagnosis due to computing limitations at the time, MYCIN demonstrated that expert knowledge could be represented computationally and established a foundation for more advanced machine learning and knowledge base systems.
The document discusses expert systems, which are computer applications that solve complex problems at a human expert level. It describes the characteristics and capabilities of expert systems, why they are useful, and their key components - knowledge base, inference engine, and user interface. The document also outlines common applications of expert systems and the general development process.
The Dempster-Shafer Theory was developed by Arthur Dempster in 1967 and Glenn Shafer in 1976 as an alternative to Bayesian probability. It allows one to combine evidence from different sources and obtain a degree of belief (or probability) for some event. The theory uses belief functions and plausibility functions to represent degrees of belief for various hypotheses given certain evidence. It was developed to describe ignorance and consider all possible outcomes, unlike Bayesian probability which only considers single evidence. An example is given of using the theory to determine the murderer in a room with 4 people where the lights went out.
A Bayesian network is a probabilistic graphical model that represents conditional dependencies among random variables using a directed acyclic graph. It consists of nodes representing variables and directed edges representing causal relationships. Each node contains a conditional probability table that quantifies the effect of its parent nodes on that variable. Bayesian networks can be used to calculate the probability of events occurring based on the network structure and conditional probability tables, such as computing the probability of an alarm sounding given that no burglary or earthquake occurred but two neighbors called.
This document discusses knowledge-based agents in artificial intelligence. It defines knowledge-based agents as agents that maintain an internal state of knowledge, reason over that knowledge, update their knowledge based on observations, and take actions. Knowledge-based agents have two main components: a knowledge base that stores facts about the world, and an inference system that applies logical rules to deduce new information from the knowledge base. The document also describes the architecture of knowledge-based agents and different approaches to designing them.
A rule-based system uses predefined rules to make logical deductions and choices to perform automated actions. It consists of a database of rules representing knowledge, a database of facts as inputs, and an inference engine that controls the process of deriving conclusions by applying rules to facts. A rule-based system mimics human decision making by applying rules in an "if-then" format to incoming data to perform actions, but unlike AI it does not learn or adapt on its own.
This document discusses formal logic and its applications in AI and machine learning. It begins by explaining why logic is useful in complex domains or with little data. It then describes logic-based approaches to AI that use symbolic reasoning as an alternative to machine learning. The document proceeds to explain propositional logic and first-order logic, noting how first-order logic improves on propositional logic by allowing variables. It also mentions other logics and their applications in areas like automated discovery, inductive programming, and verification of computer systems and machine learning models.
The document discusses production systems, which are rule-based systems used in artificial intelligence to model intelligent behavior. A production system consists of a global database, set of production rules, and control system. The rules fire to modify the database based on conditions. Different control strategies are used to determine which rules fire. Production systems are modular and allow knowledge representation as condition-action rules. Examples of applications in problem solving are provided.
The document discusses game playing in artificial intelligence. It describes how general game playing (GGP) involves designing AI that can play multiple games by learning the rules, rather than being programmed for a specific game. The document outlines how the minimax algorithm is commonly used for game playing, involving move generation and static evaluation functions to search game trees and determine the best move by maximizing or minimizing values at each level.
A study on “Diagnosis Test of Diabetics and Hypertension by AI”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
A study on “Impact of Artificial Intelligence in COVID-19 Diagnosis”, Presentation slides for International Conference on "Life Sciences: Acceptance of the New Normal", St. Aloysius' College, Jabalpur, Madhya Pradesh, India, 27-28 August, 2021
A study on “impact of artificial intelligence in covid19 diagnosis”Dr. C.V. Suresh Babu
Although the lungs are one of the most vital organs in the body, they are vulnerable to infection and injury. COVID-19 has put the entire world in an unprecedented difficult situation, bringing life to a halt and claiming thousands of lives all across the world. Medical imaging, such as X-rays and computed tomography (CT), is essential in the global fight against COVID-19, and newly emerging artificial intelligence (AI) technologies are boosting the power of imaging tools and assisting medical specialists. AI can improve job efficiency by precisely identifying infections in X-ray and CT images and allowing further measurement. We focus on the integration of AI with X-ray and CT, both of which are routinely used in frontline hospitals, to reflect the most recent progress in medical imaging and radiology combating COVID-19.
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1. Department of Information Technology 1Soft Computing (ITC4256 )
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
Associative memory network
2. Department of Information Technology 2Soft Computing (ITC4256 )
Action Plan
• Associative Memory Networks
- Introduction to auto associative memory network
- Auto associative memory architecture
- Auto associative memory training & testing algorithm
- Introduction to hetero associative memory network
- Hetero associative memory architecture
- Hetero associative memory training & testing algorithm
• Quiz at the end of session
3. Department of Information Technology 3Soft Computing (ITC4256 )
Associative Memory Networks
• These kinds of neural networks work on the basis of pattern association, which means they can store
different patterns and at the time of giving an output they can produce one of the stored patterns by
matching them with the given input pattern.
• These types of memories are also called Content-Addressable Memory CAM.
4. Department of Information Technology 4Soft Computing (ITC4256 )
Auto Associative Memory - Architecture
• This is a single layer neural network in which the input training vector and the output target vectors
are the same.
• As shown in the following figure, the architecture of Auto Associative memory network has ‘n’
number of input training vectors and similar ‘n’ number of output target vectors.
5. Department of Information Technology 5Soft Computing (ITC4256 )
Auto Associative Memory – Training Algorithm
For training, this network is using the Hebb or Delta learning rule.
Step 1 − Initialize all the weights to zero as wij = 0, i = 1 to n, j = 1 to n
Step 2 − Perform steps 3-4 for each input vector.
Step 3 − Activate each input unit as follows −
xi = si (i = 1 to n)
Step 4 − Activate each output unit as follows −
yj = sj (j = 1 to n)
Step 5 − Adjust the weights as follows −
wij(new) = wij(old) + xiyj
6. Department of Information Technology 6Soft Computing (ITC4256 )
Auto Associative Memory – Testing Algorithm
Step 1 − Set the weights obtained during training for Hebb’s rule.
Step 2 − Perform steps 3-5 for each input vector.
Step 3 − Set the activation of the input units equal to that of the input vector.
Step 4 − Calculate the net input to each output unit j = 1 to n
n
yinj = ∑ xiwij
i=1
Step 5 − Apply the following activation function to calculate the output
yj = f(yinj) = +1 if yinj > 0
- 1 if yinj ⩽ 0
7. Department of Information Technology 7Soft Computing (ITC4256 )
Hetero Associative Memory
• Similar to Auto Associative Memory network, this is also a single layer
neural network.
• The weights are determined so that the network stores a set of patterns.
8. Department of Information Technology 8Soft Computing (ITC4256 )
Hetero Associative Memory - Architecture
• As shown in the following figure, the architecture of Hetero Associative Memory network has ‘n’
number of input training vectors and ‘m’ number of output target vectors.
9. Department of Information Technology 9Soft Computing (ITC4256 )
Hetero Associative Memory – Training Algorithm
For training, this network is using the Hebb or Delta learning rule.
Step 1 − Initialize all the weights to zero as wij = 0, i = 1 to n, j = 1 to m
Step 2 − Perform steps 3-4 for each input vector.
Step 3 − Activate each input unit as follows −
xi = si (i = 1 to n)
Step 4 − Activate each output unit as follows −
yj = sj (j = 1 to m)
Step 5 − Adjust the weights as follows −
wij(new) = wij(old) + xiyj
10. Department of Information Technology 10Soft Computing (ITC4256 )
Hetero Associative Memory – Testing Algorithm
Step 1 − Set the weights obtained during training for Hebb’s rule.
Step 2 − Perform steps 3-5 for each input vector.
Step 3 − Set the activation of the input units equal to that of the input vector.
Step 4 − Calculate the net input to each output unit j = 1 to m
n
yinj = ∑ xiwij
i=1
Step 5 − Apply the following activation function to calculate the output
+1 if yinj > 0
yj = f(yinj) = 0 if yinj = 0
- 1 if yinj < 0
11. Department of Information Technology 11Soft Computing (ITC4256 )
Quiz - Questions
1. What is the other name of associative memory?
2. In which associative memory network, the input training vector and the
output target vectors are the same?
a) auto b) hetero c) iterative d) noniterative
3. In which associative memory network, the input training vector and the
output target vectors are not the same?
a) auto b) hetero c) iterative d) noniterative
4. For which algorithm does the associative memory networks use the Hebb or
Delta learning rule?
a) training b) testing c) processing d) none
5. For which algorithm does the associative memory networks set the
activation of the input units equal to that of the input vector.
a) training b) testing c) processing d) none
12. Department of Information Technology 12Soft Computing (ITC4256 )
Quiz - Answers
1. What is the other name of associative memory?
Content-Addressable Memory (CAM)
2. In which associative memory network, the input training vector and the
output target vectors are the same?
a) auto
3. In which associative memory network, the input training vector and the
output target vectors are not the same?
b) hetero
4. For which algorithm does the associative memory networks use the Hebb or
Delta learning rule?
a) training
5. For which algorithm does the associative memory networks set the
activation of the input units equal to that of the input vector.
b) testing
13. Department of Information Technology 13Soft Computing (ITC4256 )
Action Plan
• Associative Memory Networks (Cont…)
- Introduction to iterative auto associative network
- Introduction to bidirectional associative network
- BAM operation
- BAM stability and storage capacity
• Quiz at the end of session
• Assignment – 2: Write a detailed note on iterative auto associative memory.
14. Department of Information Technology 14Soft Computing (ITC4256 )
Iterative Auto Associative Network
• Net does not respond to the input signal with the stored target pattern.
• Respond like stored pattern.
• Use the first response as input to the net again.
• Iterative auto associative network recover original stored vector when presented with test vector close
to it.
• It is also known as recurrent auto associative networks.
15. Department of Information Technology 15Soft Computing (ITC4256 )
Bidirectional Associative Memory (BAM)
• Bidirectional associative memory (BAM), first proposed by Bart Kosko, is a hetero associative
network.
• It associates patterns from one set, set A, to patterns from another set, set B, and vice versa.
• Human memory is essentially associative.
• We attempt to establish a chain of associations, and thereby to restore a lost memory.
17. Department of Information Technology 17Soft Computing (ITC4256 )
BAM Operation (Cont…)
• The correlation matrix is the matrix product of the input vector X, and the transpose of the output
vector YT.
• The BAM weight matrix is the sum of all correlation matrices, that is,
where M is the number of pattern pairs to be stored in the BAM.
T
m
M
m
m YXW
1
18. Department of Information Technology 18Soft Computing (ITC4256 )
BAM Operation (Cont…)
• The input vector X (p) is applied to the transpose of weight matrix WT to produce an output vector
Y(p).
• Then, the output vector Y(p) is applied to the weight matrix W to produce a new input vector X(p+1).
• This process is repeated until input and output vector become unchanged, or in other words, the BAM
reaches stable state.
19. Department of Information Technology 19Soft Computing (ITC4256 )
Stability and Storage Capacity of the BAM
• The BAM is unconditionally stable.
• The maximum number of associations to be stored in the BAM should not
exceed the number of neurons in the smaller layer.
• The more serious problem with the BAM is incorrect convergence.
• In fact, a stable association may be only slightly related to the initial input
vector.
20. Department of Information Technology 20Soft Computing (ITC4256 )
Quiz - Questions
1. What is the other name of iterative auto associative networks?
2. BAM is a ------------ associative network.
3. What has to be created for each pattern pair in order to develop BAM?
4. The major issue with BAM is ------------ .
5. Who first proposed BAM?
21. Department of Information Technology 21Soft Computing (ITC4256 )
Quiz - Answers
1. What is the other name of iterative auto associative networks?
Recurrent auto associative networks
2. BAM is a ------------ associative network.
Hetero
3. What has to be created for each pattern pair in order to develop BAM?
Correlation matrix
4. The major issue with BAM is ------------ .
Incorrect convergence
5. Who first proposed BAM?
Bart Kosko
22. Department of Information Technology 22Soft Computing (ITC4256 )
Action Plan
• Associative Memory Networks (Cont…)
- Introduction to Hopfield networks
- Introduction to Discrete Hopfield networks
- Discrete Hopfield networks training & testing algorithm
- Energy function evaluation
- Introduction to Continuous Hopfield networks
• Quiz at the end of session
23. Department of Information Technology 23Soft Computing (ITC4256 )
Hopfield Networks
• The Hopfield network represents an auto-associative type of memory.
• Hopfield neural network was invented by Dr. John J. Hopfield in 1982.
• It consists of a single layer which contains one or more fully connected
recurrent neurons.
24. Department of Information Technology 24Soft Computing (ITC4256 )
Discrete Hopfield Network
• The network has symmetrical weights with no self-connections i.e., wij =
wji and wii = 0.
Architecture
• Following are some important points to keep in mind about discrete
Hopfield network −
- This model consists of neurons with one inverting and one non-
inverting output.
- The output of each neuron should be the input of other neurons
but not the input of self.
25. Department of Information Technology 25Soft Computing (ITC4256 )
Discrete Hopfield Network (Cont…)
- Weight/connection strength is represented by wij.
- Weights should be symmetrical, i.e. wij = wji
• The output from Y1 going to Y2, Yi and Yn have the weights w12, w1i and
w1n respectively. Similarly, other arcs have the weights on them.
26. Department of Information Technology 26Soft Computing (ITC4256 )
Discrete Hopfield Network – Training Algorithm
• During training of discrete Hopfield network, weights will be updated.
• As we know that we can have the binary input vectors as well as bipolar
input vectors.
• Hence, in both the cases, weight updates can be done with the following
relation:
Case 1 − Binary input patterns
For a set of binary patterns s p, p = 1 to P
Here, s p = s1 p, s2 p,..., si p,..., sn p
Weight Matrix is given by
P
wij = ∑ [2si(p)−1][2sj(p)−1] for i ≠ j
p=1
27. Department of Information Technology 27Soft Computing (ITC4256 )
Discrete Hopfield Network – Training Algorithm
Case 2 − Bipolar input patterns
For a set of binary patterns s p, p = 1 to P
Here, s p = s1 p, s2 p,..., si p,..., sn p
Weight Matrix is given by
P
wij = ∑ [si(p)][sj(p)] for i ≠ j
p=1
28. Department of Information Technology 28Soft Computing (ITC4256 )
Discrete Hopfield Network – Testing Algorithm
Step 1 − Initialize the weights, which are obtained from training algorithm by
using Hebbian principle.
Step 2 − Perform steps 3-9, if the activations of the network is not
consolidated.
Step 3 − For each input vector X, perform steps 4-8.
Step 4 − Make initial activation of the network equal to the external input
vector X as follows −
yi = xi for i = 1 to n
Step 5 − For each unit Yi, perform steps 6-9.
29. Department of Information Technology 29Soft Computing (ITC4256 )
Discrete Hopfield Network – Testing Algorithm
Step 6 − Calculate the net input of the network as follows −
yini=xi+∑ yjwji
j
Step 7 − Apply the activation as follows over the net input to calculate the output −
1 if yini > θi
yi = yi if yini = θi
0 if yini < θi
Here θi is the threshold.
Step 8 − Broadcast this output yi to all other units.
Step 9 − Test the network for conjunction.
30. Department of Information Technology 30Soft Computing (ITC4256 )
Energy Function Evaluation
• An energy function is defined as a function that is bonded and non-
increasing function of the state of the system.
• Energy function Ef, also called Lyapunov function determines the stability
of discrete Hopfield network, and is characterized as follows −
n n n n
Ef = − 1 / 2 ∑ ∑ yi yj wij − ∑ xi yi + ∑ θi yi
i=1 j=1 i=1 i=1
31. Department of Information Technology 31Soft Computing (ITC4256 )
Continuous Hopfield Network
• Model − The model or architecture can be build up by adding electrical
components such as amplifiers which can map the input voltage to the output
voltage over a sigmoid activation function.
• Energy Function Evaluation
n n n n n yi
Ef = 1 / 2 ∑ ∑ yiyjwij − ∑ xiyi + 1 / λ ∑ ∑ wijgri ∫ a−1(y)dy
i=1 j=1 i=1 i=1 j=1 0
j≠i j≠i
• Here λ is gain parameter and gri input conductance.
32. Department of Information Technology 32Soft Computing (ITC4256 )
Quiz - Questions
1. The Hopfield network is an ---------- associative type of memory.
2. Hopfield consists of a -------- layer which contains one or more fully
connected recurrent neurons.
a) single b) double c) triple d) linear
3. Which principle is used to initialize weights in testing algorithm?
4. What is the other name of energy function?
5. Continuous Hopfield network has --------- as a continuous variable.
a) weight b) time c) bias d) none
33. Department of Information Technology 33Soft Computing (ITC4256 )
Quiz - Answers
1. The Hopfield network is an ---------- associative type of memory.
Auto
2. Hopfield consists of a -------- layer which contains one or more fully
connected recurrent neurons.
a) single
3. Which principle is used to initialize weights in testing algorithm?
Hebbian principle
4. What is the other name of energy function?
Lyapunov function
5. Continuous Hopfield network has --------- as a continuous variable.
b) time