Classification using back propagation algorithm
Download as ppt, pdf1 like3,317 views
The document describes the back-propagation learning algorithm used in neural networks, emphasizing its importance and application in multilayer feed-forward networks. It explains the architecture of these networks, the training process using input signals and corresponding target outputs, and the error propagation mechanism to adjust weights. Various real-world applications, such as handwritten character recognition and laboratory medicine, showcase the algorithm's effectiveness.
1 of 24
Downloaded 24 times
Ad
Recommended
MD5 ALGORITHM.pptx
MD5 ALGORITHM.pptxRajapriya82 The MD5 algorithm is a cryptographic hash function that generates a 128-bit digest from a message of any length. It works by first padding the message to a length that is a multiple of 512 bits, then appending the message length. It initializes buffers and processes the message in 512-bit blocks using four rounds of 16 operations each, applying different functions to update the buffers. The final buffer values become the MD5 digest. It is commonly used to verify file integrity but is now considered insecure due to possible hash collisions.
Algorithms and Flowcharts
Algorithms and FlowchartsDeva Singh The document outlines fundamental concepts of computer science focusing on algorithms and flowcharts, including their definitions, phases, and examples of algorithms using pseudo code. It explains how to develop algorithms step-by-step and illustrates the use of flowcharts to represent program logic visually. Additionally, it covers decision structures and relational operators in algorithm design, providing numerous examples to demonstrate the principles involved.
Open Source Software
Open Source Software Ali Yavari Open source software refers to software with source code that is made freely available and may be redistributed and modified. Key points:
- Open source software source code is openly shared so it can be collaboratively developed and improved by a community.
- Popular open source programs include the Linux operating system, Apache web server, MySQL database, PHP, WordPress, VLC media player, Notepad++, Ubuntu, Android, GIMP, LaTeX and Arduino.
- Open source software is often free to use but may also be used commercially. It promotes sharing, collaboration and continual improvements to software.
Measurement in research
Measurement in researchBikram Pradhan 1. Measurement involves assigning numbers to objects or observations based on established rules. There are different scales of measurement that determine what statistical analyses can be used.
2. The scales of measurement from least to most powerful are nominal, ordinal, interval, and ratio scales. Nominal scales simply categorize data while ratio scales have a true zero point and allow comparisons of ratios.
3. Each scale of measurement is associated with different statistical analyses that can appropriately be used. For example, only nominal data allows the use of the mode as a measure of central tendency while more powerful scales like interval and ratio allow the use of more sophisticated tests.
Perceptron & Neural Networks
Perceptron & Neural NetworksNAGUR SHAREEF SHAIK 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.
Mobile tower radiation and its impact on environment
Mobile tower radiation and its impact on environmentNishat Fatima Mobile tower radiation can have hazardous health and environmental effects. Mobile towers emit high-frequency radio waves or microwaves that can travel long distances. Different countries have set different safety limits for radiation exposure. Exposure limits in India are outlined in the Indian Wireless Act and Telegraph Act. Radiation can impact humans, plants and animals. It has been linked to headaches, genetic damage, and increased cancer risks in humans. Birds and bees can experience disorientation and declining populations near mobile towers. Reducing mobile phone use and carrying phones on the body can help lower exposure risks. Stricter safety regulations and siting of mobile towers are needed to protect public health and the environment from electromagnetic radiation pollution.
Concurrency control
Concurrency controlSoumyajit Dutta Concurrency control mechanisms use various protocols like lock-based, timestamp-based, and validation-based to maintain database consistency when transactions execute concurrently. Lock-based protocols use locks on data items to control concurrent access, with two-phase locking being a common approach. Timestamp-based protocols order transactions based on timestamps to ensure serializability. Validation-based protocols validate that a transaction's writes do not violate serializability before committing its writes.
Role of artificial intelligence in health care
Role of artificial intelligence in health carePrachi Gupta Artificial intelligence has many applications in healthcare, including improving disease diagnosis through analysis of medical imaging and other patient data, aiding radiologists in detecting abnormalities, and enabling constant remote patient monitoring. The use of AI is expected to lower medical costs through greater accuracy and better predictive analysis. It is being applied to issues like managing the coronavirus outbreak through monitoring patients and regulating hospital visitor flow. Going forward, AI may help predict where virus outbreaks are likely to occur.
Support Vector Machines ( SVM )
Support Vector Machines ( SVM ) Mohammad Junaid Khan The document provides an overview of support vector machines (SVM), detailing their role as classifiers that output optimal hyperplanes for categorizing data points through supervised learning. It explains concepts such as linear vs nonlinear separability, margin, and key tuning parameters like kernel, regularization, and gamma. Additionally, it highlights the pros and cons of SVMs and their applications in various fields, including face detection and text categorization.
Mc culloch pitts neuron
Mc culloch pitts neuronSiksha 'O' Anusandhan (Deemed to be University ) 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.
backpropagation in neural networks
backpropagation in neural networksAkash Goel Backpropagation is a learning algorithm for multi-layer neural networks, developed in 1969, which adjusts weights based on the error of the output compared to expected results. The process consists of a forward pass to determine output and a backward pass to calculate weight adjustments through gradient descent. Neural networks, which leverage backpropagation, have applications in classification and function approximation, thriving in scenarios with ample training data and where explicit rules are hard to define.
Recognition-of-tokens
Recognition-of-tokensDattatray Gandhmal The document discusses the process of recognizing tokens in a programming language, detailing how to specify tokens and write a program to recognize them. It introduces transition diagrams, which describe states and transitions in a deterministic manner, showing how each token, whitespace, and other inputs are managed. Additionally, it explains how to handle and return token values through a structured approach.
Feedforward neural network
Feedforward neural networkSopheaktra YONG The document discusses feedforward neural networks, specifically multilayer perceptrons (MLPs), explaining their structure, functioning, and the various types of activation functions used. It covers the perceptron model, its limitations with non-linearly separable data such as the XOR problem, and methods to prevent overfitting in MLPs. Additionally, it touches on techniques for adjusting weights and the backpropagation algorithm to improve learning accuracy.
Machine Learning: Introduction to Neural Networks
Machine Learning: Introduction to Neural NetworksFrancesco Collova' 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.
Back propagation
Back propagationNagarajan 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.
Ensemble methods in machine learning
Ensemble methods in machine learningSANTHOSH RAJA M G Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.
Adaline madaline
Adaline madalineNagarajan 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
Artificial Neural Network ANN
Artificial Neural Network ANNAbdullah al Mamun Artificial neural networks (ANNs) are computational models inspired by the brain, utilizing interconnected 'neurons' to process information in parallel. They are used for various applications, including classification, noise reduction, and prediction, and can learn from examples, adapt, and generalize. Despite their advantages, such as flexibility and robustness, they involve complex structures and cannot easily explain their workings.
Machine Learning with Decision trees
Machine Learning with Decision treesKnoldus Inc. The document discusses decision trees as a fundamental supervised learning algorithm, outlining their structure, appropriate problem types, and the concepts of entropy and information gain. It addresses common challenges such as overfitting and pruning strategies to improve model performance. The document also highlights the importance of careful tree growth management and validation to ensure accuracy in classifications.
Hetro associative memory
Hetro associative memoryDEEPENDRA KORI Hetero associative memory is a type of associative memory that allows the learning and recalling of relationships between different sets of items using a static neural network. It employs training algorithms such as Hebb's rule or the delta rule to adjust weights between input and output patterns, enabling the network to store and retrieve multiple patterns. The document details the training and testing processes for a neural network designed to associate various input patterns with corresponding outputs.
Adaptive Resonance Theory
Adaptive Resonance TheoryNaveen Kumar The document provides an overview of Adaptive Resonance Theory (ART), a family of unsupervised neural networks developed by Stephen Grossberg that addresses the plasticity-stability dilemma in learning systems. It explains the architecture and functioning of ART, including the roles of various components such as the comparison field, recognition field, and vigilance parameter in categorizing input patterns. The document also describes different versions of ART, including ART1, ART2, and ART3, and their applications in various fields such as robotics and medical diagnosis.
Artifical Neural Network and its applications
Artifical Neural Network and its applicationsSangeeta Tiwari The document discusses Artificial Neural Networks (ANN), exploring their structure, function, and comparison with biological neurons. It covers topics such as perceptrons, feedforward neural networks, the backpropagation algorithm, and deep learning applications, along with advantages and disadvantages of ANNs. The document aims to provide a comprehensive understanding of ANNs and their future in machine learning and artificial intelligence.
Artificial Neural Networks Lect3: Neural Network Learning rules
Artificial Neural Networks Lect3: Neural Network Learning rulesMohammed Bennamoun 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.
Decision tree induction \ Decision Tree Algorithm with Example| Data science
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6 A decision tree is a supervised machine learning algorithm represented as a flowchart-like structure with nodes, branches, and leaf nodes, where each internal node denotes a test on an attribute, branches denote test outcomes, and leaf nodes represent class labels. The ID3 algorithm, developed by J. Ross Quinlan, uses information gain to determine the best attribute for splitting data and is popular due to its ability to handle multidimensional data without extensive data cleaning. Decision trees perform classification quickly and effectively, accommodating both categorical and continuous variables.
Unsupervised learning clustering
Unsupervised learning clusteringArshad Farhad This document discusses unsupervised learning and clustering. It defines unsupervised learning as modeling the underlying structure or distribution of input data without corresponding output variables. Clustering is described as organizing unlabeled data into groups of similar items called clusters. The document focuses on k-means clustering, describing it as a method that partitions data into k clusters by minimizing distances between points and cluster centers. It provides details on the k-means algorithm and gives examples of its steps. Strengths and weaknesses of k-means clustering are also summarized.
Huffman Coding
Huffman Codinganithabalaprabhu The document provides an overview of Huffman coding, a lossless data compression algorithm. It begins with a simple example to illustrate the basic idea of assigning shorter codes to more frequent symbols. It then defines key terms like entropy and describes the Huffman coding algorithm, which constructs an optimal prefix code from the frequency of symbols in the data. The document discusses how Huffman coding can be applied to image compression by first predicting pixel values and then encoding the residuals. It notes some disadvantages of Huffman coding and describes variations like adaptive Huffman coding.
Neural Networks
Neural NetworksNikitaRuhela 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.
Frames
Framesamitp26 The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Artificial neural network
Artificial neural networkmustafa aadel 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.
Classification by back propagation, multi layered feed forward neural network...
Classification by back propagation, multi layered feed forward neural network...bihira aggrey The document discusses classification using backpropagation in multilayer feedforward neural networks, highlighting the components and functionalities of these systems. It emphasizes the learning process, where neural networks adjust weights to minimize prediction errors, particularly in classification or numeric prediction tasks. Key aspects include the architecture of neurons, the iterative learning algorithm, and the challenges of interpretability and training time.
More Related Content
What's hot (20)
Support Vector Machines ( SVM )
Support Vector Machines ( SVM ) Mohammad Junaid Khan The document provides an overview of support vector machines (SVM), detailing their role as classifiers that output optimal hyperplanes for categorizing data points through supervised learning. It explains concepts such as linear vs nonlinear separability, margin, and key tuning parameters like kernel, regularization, and gamma. Additionally, it highlights the pros and cons of SVMs and their applications in various fields, including face detection and text categorization.
Mc culloch pitts neuron
Mc culloch pitts neuronSiksha 'O' Anusandhan (Deemed to be University ) 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.
backpropagation in neural networks
backpropagation in neural networksAkash Goel Backpropagation is a learning algorithm for multi-layer neural networks, developed in 1969, which adjusts weights based on the error of the output compared to expected results. The process consists of a forward pass to determine output and a backward pass to calculate weight adjustments through gradient descent. Neural networks, which leverage backpropagation, have applications in classification and function approximation, thriving in scenarios with ample training data and where explicit rules are hard to define.
Recognition-of-tokens
Recognition-of-tokensDattatray Gandhmal The document discusses the process of recognizing tokens in a programming language, detailing how to specify tokens and write a program to recognize them. It introduces transition diagrams, which describe states and transitions in a deterministic manner, showing how each token, whitespace, and other inputs are managed. Additionally, it explains how to handle and return token values through a structured approach.
Feedforward neural network
Feedforward neural networkSopheaktra YONG The document discusses feedforward neural networks, specifically multilayer perceptrons (MLPs), explaining their structure, functioning, and the various types of activation functions used. It covers the perceptron model, its limitations with non-linearly separable data such as the XOR problem, and methods to prevent overfitting in MLPs. Additionally, it touches on techniques for adjusting weights and the backpropagation algorithm to improve learning accuracy.
Machine Learning: Introduction to Neural Networks
Machine Learning: Introduction to Neural NetworksFrancesco Collova' 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.
Back propagation
Back propagationNagarajan 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.
Ensemble methods in machine learning
Ensemble methods in machine learningSANTHOSH RAJA M G Ensemble methods combine multiple machine learning models to obtain better predictive performance than from any individual model. There are two main types of ensemble methods: sequential (e.g AdaBoost) where models are generated one after the other, and parallel (e.g Random Forest) where models are generated independently. Popular ensemble methods include bagging, boosting, and stacking. Bagging averages predictions from models trained on random samples of the data, while boosting focuses on correcting previous models' errors. Stacking trains a meta-model on predictions from other models to produce a final prediction.
Adaline madaline
Adaline madalineNagarajan 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
Artificial Neural Network ANN
Artificial Neural Network ANNAbdullah al Mamun Artificial neural networks (ANNs) are computational models inspired by the brain, utilizing interconnected 'neurons' to process information in parallel. They are used for various applications, including classification, noise reduction, and prediction, and can learn from examples, adapt, and generalize. Despite their advantages, such as flexibility and robustness, they involve complex structures and cannot easily explain their workings.
Machine Learning with Decision trees
Machine Learning with Decision treesKnoldus Inc. The document discusses decision trees as a fundamental supervised learning algorithm, outlining their structure, appropriate problem types, and the concepts of entropy and information gain. It addresses common challenges such as overfitting and pruning strategies to improve model performance. The document also highlights the importance of careful tree growth management and validation to ensure accuracy in classifications.
Hetro associative memory
Hetro associative memoryDEEPENDRA KORI Hetero associative memory is a type of associative memory that allows the learning and recalling of relationships between different sets of items using a static neural network. It employs training algorithms such as Hebb's rule or the delta rule to adjust weights between input and output patterns, enabling the network to store and retrieve multiple patterns. The document details the training and testing processes for a neural network designed to associate various input patterns with corresponding outputs.
Adaptive Resonance Theory
Adaptive Resonance TheoryNaveen Kumar The document provides an overview of Adaptive Resonance Theory (ART), a family of unsupervised neural networks developed by Stephen Grossberg that addresses the plasticity-stability dilemma in learning systems. It explains the architecture and functioning of ART, including the roles of various components such as the comparison field, recognition field, and vigilance parameter in categorizing input patterns. The document also describes different versions of ART, including ART1, ART2, and ART3, and their applications in various fields such as robotics and medical diagnosis.
Artifical Neural Network and its applications
Artifical Neural Network and its applicationsSangeeta Tiwari The document discusses Artificial Neural Networks (ANN), exploring their structure, function, and comparison with biological neurons. It covers topics such as perceptrons, feedforward neural networks, the backpropagation algorithm, and deep learning applications, along with advantages and disadvantages of ANNs. The document aims to provide a comprehensive understanding of ANNs and their future in machine learning and artificial intelligence.
Artificial Neural Networks Lect3: Neural Network Learning rules
Artificial Neural Networks Lect3: Neural Network Learning rulesMohammed Bennamoun 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.
Decision tree induction \ Decision Tree Algorithm with Example| Data science
Decision tree induction \ Decision Tree Algorithm with Example| Data scienceMaryamRehman6 A decision tree is a supervised machine learning algorithm represented as a flowchart-like structure with nodes, branches, and leaf nodes, where each internal node denotes a test on an attribute, branches denote test outcomes, and leaf nodes represent class labels. The ID3 algorithm, developed by J. Ross Quinlan, uses information gain to determine the best attribute for splitting data and is popular due to its ability to handle multidimensional data without extensive data cleaning. Decision trees perform classification quickly and effectively, accommodating both categorical and continuous variables.
Unsupervised learning clustering
Unsupervised learning clusteringArshad Farhad This document discusses unsupervised learning and clustering. It defines unsupervised learning as modeling the underlying structure or distribution of input data without corresponding output variables. Clustering is described as organizing unlabeled data into groups of similar items called clusters. The document focuses on k-means clustering, describing it as a method that partitions data into k clusters by minimizing distances between points and cluster centers. It provides details on the k-means algorithm and gives examples of its steps. Strengths and weaknesses of k-means clustering are also summarized.
Huffman Coding
Huffman Codinganithabalaprabhu The document provides an overview of Huffman coding, a lossless data compression algorithm. It begins with a simple example to illustrate the basic idea of assigning shorter codes to more frequent symbols. It then defines key terms like entropy and describes the Huffman coding algorithm, which constructs an optimal prefix code from the frequency of symbols in the data. The document discusses how Huffman coding can be applied to image compression by first predicting pixel values and then encoding the residuals. It notes some disadvantages of Huffman coding and describes variations like adaptive Huffman coding.
Neural Networks
Neural NetworksNikitaRuhela 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.
Frames
Framesamitp26 The document discusses different knowledge representation schemes used in artificial intelligence systems. It describes semantic networks, frames, propositional logic, first-order predicate logic, and rule-based systems. For each technique, it provides facts about how knowledge is represented and examples to illustrate their use. The goal of knowledge representation is to encode knowledge in a way that allows inferencing and learning of new knowledge from the facts stored in the knowledge base.
Similar to Classification using back propagation algorithm (20)
Artificial neural network
Artificial neural networkmustafa aadel 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.
Classification by back propagation, multi layered feed forward neural network...
Classification by back propagation, multi layered feed forward neural network...bihira aggrey The document discusses classification using backpropagation in multilayer feedforward neural networks, highlighting the components and functionalities of these systems. It emphasizes the learning process, where neural networks adjust weights to minimize prediction errors, particularly in classification or numeric prediction tasks. Key aspects include the architecture of neurons, the iterative learning algorithm, and the challenges of interpretability and training time.
Back_propagation_algorithm.Back_propagation_algorithm.Back_propagation_algorithm
Back_propagation_algorithm.Back_propagation_algorithm.Back_propagation_algorithmsureshkumarece1 The document explains the concept of neural networks, particularly artificial neural networks, and their applications in fields like speech recognition and image analysis. It details how multi-layer neural networks operate, including the backpropagation algorithm, which adjusts weights for learning by minimizing prediction errors. Additionally, it discusses the efficiency, strengths, and weaknesses of neural networks, as well as methods for rule extraction and sensitivity analysis.
Intro to Deep learning - Autoencoders
Intro to Deep learning - Autoencoders Akash Goel This document provides an overview of autoencoders and their use in unsupervised learning for deep neural networks. It discusses the history and development of neural networks, including early work in the 1940s-1980s and more recent advances in deep learning. It then explains how autoencoders work by setting the target values equal to the inputs, describes variants like denoising autoencoders, and how stacking autoencoders can create deep architectures for tasks like document retrieval, facial recognition, and signal denoising.
Lec 6-bp
Lec 6-bpTaymoor Nazmy 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.
MNN
MNNInternational Islamic University The document describes a multilayer neural network presentation. It discusses key concepts of neural networks including their architecture, types of neural networks, and backpropagation. The key points are:
1) Neural networks are composed of interconnected processing units (neurons) that can learn relationships in data through training. They are inspired by biological neural systems.
2) Common network architectures include multilayer perceptrons and recurrent networks. Backpropagation is commonly used to train multilayer feedforward networks by propagating errors backwards.
3) Neural networks have advantages like the ability to model complex nonlinear relationships, adapt to new data, and extract patterns from imperfect data. They are well-suited for problems like classification.
Unit ii supervised ii
Unit ii supervised iiIndira Priyadarsini 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.
Electricity Demand Forecasting Using ANN
Electricity Demand Forecasting Using ANNNaren Chandra Kattla This document discusses using an artificial neural network to forecast electricity demand. It describes preprocessing data, creating a feed-forward neural network model with input, hidden and output layers, and training the model using backpropagation and incremental training. The model is trained on 80% of the data and tested on the remaining 20%. Mean square error is used to evaluate accuracy on both the training and test sets, with a lower error on the test set indicating better generalization of the model to new data. The goal is to accurately forecast future electricity demand based on input variables like population, GDP, price indexes, and past consumption data.
Artificial Neural Network for machine learning
Artificial Neural Network for machine learning2303oyxxxjdeepak Artificial Neural Networks (ANNs) are computational models inspired by the human brain, consisting of interconnected nodes (neurons) organized in layers, and are used in various applications like image recognition and predictive analytics. The document explains essential components of ANNs, including training processes like forward propagation and backpropagation, activation functions, and optimization algorithms such as gradient descent. It also covers specific types of networks like perceptrons, multilayer perceptrons, and Adaline, which illustrate their structure and training methodologies, highlighting the importance of network tuning for optimal performance.
Multi Layer Network
Multi Layer NetworkInternational Islamic University 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.
lecture07.ppt
lecture07.pptbutest The document provides an overview of artificial neural networks and supervised learning techniques. It discusses the biological inspiration for neural networks from neurons in the brain. Single-layer perceptrons and multilayer backpropagation networks are described for classification tasks. Methods to accelerate learning such as momentum and adaptive learning rates are also summarized. Finally, it briefly introduces recurrent neural networks like the Hopfield network for associative memory applications.
Web spam classification using supervised artificial neural network algorithms
Web spam classification using supervised artificial neural network algorithmsaciijournal This paper evaluates the performance of three artificial neural network algorithms (conjugate gradient, resilient back-propagation, and Levenberg-Marquardt) for web spam classification. It illustrates the necessity for effective spam detection techniques due to the evolving technologies used by spammers and analyzes low-cost features utilized in the classification process. The study confirms that neural network-based classifiers can effectively address web spam challenges by adjusting weights and training based on performance metrics.
2.5 backpropagation
2.5 backpropagationKrish_ver2 The document discusses artificial neural networks and classification using backpropagation, describing neural networks as sets of connected input and output units where each connection has an associated weight. It explains backpropagation as a neural network learning algorithm that trains networks by adjusting weights to correctly predict the class label of input data, and how multi-layer feed-forward neural networks can be used for classification by propagating inputs through hidden layers to generate outputs.
Neural-Networks.ppt
Neural-Networks.pptRINUSATHYAN The document discusses different types of machine learning paradigms including supervised learning, unsupervised learning, and reinforcement learning. It then provides details on artificial neural networks, describing them as consisting of simple processing units that communicate through weighted connections, similar to neurons in the human brain. The document outlines key aspects of artificial neural networks like processing units, connections between units, propagation rules, and learning methods.
CST413 KTU S7 CSE Machine Learning Neural Networks and Support Vector Machine...
CST413 KTU S7 CSE Machine Learning Neural Networks and Support Vector Machine...resming1 This module covers neural Networks and support vector machines.
Web Spam Classification Using Supervised Artificial Neural Network Algorithms
Web Spam Classification Using Supervised Artificial Neural Network Algorithmsaciijournal This paper evaluates three supervised learning algorithms—conjugate gradient, resilient back-propagation, and Levenberg-Marquardt—for web spam classification using artificial neural networks. The study highlights the algorithms' performance based on classification accuracy and training efficiency, with a focus on addressing challenges posed by evolving web spam techniques. It provides an analysis of various features and presents performance metrics for each algorithm through experimental results.
Web Spam Classification Using Supervised Artificial Neural Network Algorithms
Web Spam Classification Using Supervised Artificial Neural Network Algorithmsaciijournal The paper discusses the need for effective classifiers in web spam detection due to evolving spam tactics and evaluates three artificial neural network algorithms: conjugate gradient, resilient back-propagation, and Levenberg-Marquardt. The authors conduct experiments using a dataset of web pages categorized as spam or ham to assess the performance of these algorithms based on several metrics, including sensitivity, specificity, and accuracy. It highlights the algorithms' capability to classify web spam effectively while also considering computational requirements.
Backpropagation.pptx
Backpropagation.pptxVandanaVipparthi The document discusses backpropagation, which is a popular neural network learning algorithm. It describes the key components of a neural network including the input, hidden, and output layers. During training, weights are adjusted to minimize error between the network's predictions and actual outputs. Backpropagation works by propagating error backwards from the output layer through hidden layers to update weights and biases using gradient descent. This helps the network learn and improve its ability to accurately predict the class labels of new input samples.
22PCOAM16 _ML_ Unit 2 Full unit notes.pdf
22PCOAM16 _ML_ Unit 2 Full unit notes.pdfGuru Nanak Technical Institutions 22 PCOAM16 _ML_ Unit 2 Full unit notes.pdf
22PCOAM16 ML UNIT 2 NOTES & QB QUESTION WITH ANSWERS
22PCOAM16 ML UNIT 2 NOTES & QB QUESTION WITH ANSWERSGuru Nanak Technical Institutions 22PCOAM16 ML UNIT 2 NOTES & QB QUESTION WITH ANSWERS
Ad
Recently uploaded (20)
Rapid Prototyping for XR: Lecture 2 - Low Fidelity Prototyping.
Rapid Prototyping for XR: Lecture 2 - Low Fidelity Prototyping.Mark Billinghurst This is lecture 2 on the Rapid Prototyping for XR course taught by Mark Billingurst on June 10th 2025. This lecture is about Low Fidelity Prototyping.
Call For Papers - 17th International Conference on Wireless & Mobile Networks...
Call For Papers - 17th International Conference on Wireless & Mobile Networks...hosseinihamid192023 17th International Conference on Wireless & Mobile Networks (WiMoNe 2025) will provide
an excellent international forum for sharing knowledge and results in theory, methodology and
applications of Wireless & Mobile computing Environment. Current information age is witnessing
a dramatic use of digital and electronic devices in the workplace and beyond. Wireless, Mobile
Networks & its applications had received a significant and sustained research interest in terms of
designing and deploying large scale and high performance computational applications in real life.
The aim of the conference is to provide a platform to the researchers and practitioners from both
academia as well as industry to meet and share cutting-edge development in the field.
Deep Learning for Image Processing on 16 June 2025 MITS.pptx
Deep Learning for Image Processing on 16 June 2025 MITS.pptxresming1 This covers how image processing or the field of computer vision has advanced with the advent of neural network architectures ranging from LeNet to Vision transformers. It covers how deep neural network architectures have developed step-by-step from the popular CNNs to ViTs. CNNs and its variants along with their features are described. Vision transformers are introduced and compared with CNNs. It also shows how an image is processed to be given as input to the vision transformer. It give the applications of computer vision.
Tally.ERP 9 at a Glance.book - Tally Solutions .pdf
Tally.ERP 9 at a Glance.book - Tally Solutions .pdfShabista Imam Tally.ERP 9 at a Glance.book, a fully completed guidance to learn tally erp 9.0
Complete guidance book of Asp.Net Web API
Complete guidance book of Asp.Net Web APIShabista Imam Unlock the full potential of modern web development with the Complete Guidance Book of ASP.NET Web API—your all-in-one resource for mastering RESTful services using Microsoft’s powerful ASP.NET Core framework. This book takes you on a step-by-step journey from beginner to expert, covering everything from routing and controllers to security, performance optimization, and real-world architecture.
Tesla-Stock-Analysis-and-Forecast.pptx (1).pptx
Tesla-Stock-Analysis-and-Forecast.pptx (1).pptxmoonsony54 this is data science ppt for tesla stock (linear regression)
Validating a Citizen Observatories enabling Platform by completing a Citizen ...
Validating a Citizen Observatories enabling Platform by completing a Citizen ...Diego López-de-Ipiña González-de-Artaza Citizen Observatories (COs) are initiatives that empower citizens to engage in data collection, analysis and interpretation in order to address various issues affecting their communities and contribute to policy-making and community development.
Thematic co-exploration is a co-production process where citizens actively participate alongside scientists and other actors in the exploration of specific themes.
Making them a reality involves addressing the following challenges:
Data quality and reliability
Engagement and retention of participants
Integration with policy and decision-making
Proposal for folders structure division in projects.pdf
Proposal for folders structure division in projects.pdfMohamed Ahmed Proposal for folders structure division in projects
Rapid Prototyping for XR: Lecture 4 - High Level Prototyping.
Rapid Prototyping for XR: Lecture 4 - High Level Prototyping.Mark Billinghurst This is lecture 4 in the course on Rapid Prototyping for XR, taught by Mark Billinghurst on June 11th, 2025. This lecture is about High Level Prototyping.
Data Structures Module 3 Binary Trees Binary Search Trees Tree Traversals AVL...
Data Structures Module 3 Binary Trees Binary Search Trees Tree Traversals AVL...resming1 This covers binary trees, binary search trees, BST insertion, deletion, tree traversals - inorder, preorder, postorder, AVL trees, AVL tree insertion, deletion, BTrees - insertion, deletion, hash tables etc.
Complete University of Calculus :: 2nd edition
Complete University of Calculus :: 2nd editionShabista Imam Master the language of change with the Complete Guidance Book of Calculus—your comprehensive resource for understanding the core concepts and applications of differential and integral calculus. Designed for high school, college, and self-study learners, this book takes a clear, intuitive approach to a subject often considered challenging.
Modern multi-proposer consensus implementations
Modern multi-proposer consensus implementationsFrançois Garillot Multi-proposer consensus protocols let multiple validators propose blocks in parallel, breaking the single-leader throughput bottleneck of classic designs. Yet the modern multi-proposer consensus implementation has grown a lot since HotStuff. THisworkshop will explore the implementation details of recent advances – DAG-based approaches like Narwhal and Sui’s Mysticeti – and reveal how implementation details translate to real-world performance gains. We focus on the nitty-gritty: how network communication patterns and data handling affect throughput and latency. New techniques such as Turbine-like block propagation (inspired by Solana’s erasure-coded broadcast) and lazy push gossip broadcasting dramatically cut communication overhead. These optimizations aren’t just theoretical – they enable modern blockchains to process over 100,000 transactions per second with finality in mere milliseconds redefining what is possible in decentralized systems.
FSE_LLM4SE1_A Tool for In-depth Analysis of Code Execution Reasoning of Large...
FSE_LLM4SE1_A Tool for In-depth Analysis of Code Execution Reasoning of Large...cl144 A Tool for In-depth Analysis of Code Execution Reasoning of Large Language Models
Rapid Prototyping for XR: Lecture 5 - Cross Platform Development
Rapid Prototyping for XR: Lecture 5 - Cross Platform DevelopmentMark Billinghurst This is lecture 5 in the course about Rapid Prototyping for XR, taught by Mark Billinghurst. This lecture is about Cross Platform Development.
FUNDAMENTALS OF COMPUTER ORGANIZATION AND ARCHITECTURE
FUNDAMENTALS OF COMPUTER ORGANIZATION AND ARCHITECTUREShabista Imam FUNDAMENTALS OF COMPUTER ORGANIZATION AND ARCHITECTURE
By : Mostafa Abd-El-Barr & Hesham El-Rewini:: wiley
A complete guidance book
Validating a Citizen Observatories enabling Platform by completing a Citizen ...
Validating a Citizen Observatories enabling Platform by completing a Citizen ...Diego López-de-Ipiña González-de-Artaza
Ad
Classification using back propagation algorithm
- 2. Backpropagation Algorithms The back-propagation learning algorithm is one of the most important developments in neural networks. Backpropagation is the generalization of the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions. This learning algorithm is applied to multilayer feed-forward networks consisting of processing elements with continuous differentiable activation functions. The networks associated with back-propagation algorithm are also called back-propagation networks(BPNs).
- 3. Backpropagation Algorithms The Aim Of The Neural Network Is To Train The Net Ot Achieve A Balance Between The Net’s Ability To Respond(memorization) And Its Ability To Give Resasonable Responses To The Input That Is Similar But Not Identical To The One That Is Use In Trianing (Generalization).
- 4. Architecture This section presents the architecture of the network that is most commonly used with the backpropagation algorithm – the multilayer feedforward network
- 5. Architecture Feedforward Network Feedforward networks often have one or more hidden layers of sigmoid neurons followed by an output layer of linear neurons. Multiple layers of neurons with nonlinear transfer functions allow the network to learn nonlinear and linear relationships between input and output vectors. The linear output layer lets the network produce values outside the range -1 to +1. On the other hand, if you want to constrain the outputs of a network (such as between 0 and 1), then the output layer should use a sigmoid transfer function (such as logsig).
- 6. Learning Algorithm: Backpropagation The following slides describes teaching process of multi-layer neural network employing backpropagation algorithm. To illustrate this process the three layer neural network with two inputs and one output,which is shown in the picture below, is used:
- 7. Learning Algorithm: Backpropagation Each neuron is composed of two units. First unit adds products of weights coefficients and input signals. The second unit realise nonlinear function, called neuron transfer (activation) function. Signal e is adder output signal, and y = f(e) is output signal of nonlinear element. Signal y is also output signal of neuron.
- 8. Learning Algorithm: Backpropagation To teach the neural network we need training data set. The training data set consists of input signals (x1 and x2 ) assigned with corresponding target (desired output) z. The network training is an iterative process. In each iteration weights coefficients of nodes are modified using new data from training data set. Modification is calculated using algorithm described below: Each teaching step starts with forcing both input signals from training set. After this stage we can determine output signals values for each neuron in each network layer.
- 9. Learning Algorithm: Backpropagation Pictures below illustrate how signal is propagating through the network, Symbols w(xm)n represent weights of connections between network input xm and neuron n in input layer. Symbols yn represents output signal of neuron n.
- 12. Learning Algorithm: Backpropagation Propagation of signals through the hidden layer. Symbols wmn represent weights of connections between output of neuron m and input of neuron n in the next layer.
- 15. Learning Algorithm: Backpropagation Propagation of signals through the output layer.
- 16. Learning Algorithm: Backpropagation In the next algorithm step the output signal of the network y is compared with the desired output value (the target), which is found in training data set. The difference is called error signal d of output layer neuron
- 17. Learning Algorithm: Backpropagation The idea is to propagate error signal d (computed in single teaching step) back to all neurons, which output signals were input for discussed neuron.
- 18. Learning Algorithm: Backpropagation The idea is to propagate error signal d (computed in single teaching step) back to all neurons, which output signals were input for discussed neuron.
- 19. Learning Algorithm: Backpropagation The weights' coefficients wmn used to propagate errors back are equal to this used during computing output value. Only the direction of data flow is changed (signals are propagated from output to inputs one after the other). This technique is used for all network layers. If propagated errors came from few neurons they are added. The illustration is below:
- 20. Learning Algorithm: Backpropagation When the error signal for each neuron is computed, the weights coefficients of each neuron input node may be modified. In formulas below df(e)/de represents derivative of neuron activation function (which weights are modified).
- 21. Learning Algorithm: Backpropagation When the error signal for each neuron is computed, the weights coefficients of each neuron input node may be modified. In formulas below df(e)/de represents derivative of neuron activation function (which weights are modified).
- 22. Learning Algorithm: Backpropagation When the error signal for each neuron is computed, the weights coefficients of each neuron input node may be modified. In formulas below df(e)/de represents derivative of neuron activation function (which weights are modified).
- 23. Backpropagation applications They have been successful on a wide array of real-world data, including handwritten character recognition, pathology and laboratory medicine, and training a computer to pronounce English text.
- 24. Backpropagation terminologies EPOCH EPOCHUPDATION TERMINATING CONDITION