This document provides an overview of artificial neural networks and pattern recognition. It discusses key topics such as:
- The basic anatomy and function of artificial neurons and how they are modeled after biological neurons.
- Different types of neural networks including feedforward networks, recurrent networks, self-organizing maps, and Hopfield networks.
- Popular supervised and unsupervised learning algorithms like backpropagation and self-organizing feature maps.
- Examples of applications like handwritten character recognition, stock price prediction, and memory recall in Hopfield networks.
The document serves as an introduction for students to understand the basic concepts and applications of artificial neural networks.
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.
Artificial Neural Network seminar presentation using ppt.Mohd Faiz
- Artificial neural networks are inspired by biological neural networks and learning processes. They attempt to mimic the workings of the brain using simple units called artificial neurons that are connected in networks.
- Learning in neural networks involves modifying the synaptic strengths between neurons through mathematical optimization techniques. The goal is to minimize an error function that measures how well the network can approximate or complete a task.
- Neural networks can learn complex nonlinear functions through training algorithms like backpropagation that determine how to adjust the synaptic weights to improve performance on the learning task.
Artificial neural networks are computer programs that can recognize patterns in data and produce models to represent that data. They are inspired by the human brain in how knowledge is acquired through learning and stored in the connections between neurons. Neural networks learn by adjusting the strengths of connections between neurons based on examples provided during training. They are able to model and learn both linear and nonlinear relationships in data.
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.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
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.
This document provides an overview of artificial neural networks (ANNs). It begins by defining ANNs as models inspired by biological neural networks in the brain that are used to estimate functions. It then describes how biological neural networks operate in the brain with interconnected neurons. The document outlines several key properties of ANNs including plasticity, learning from experience, and their use in machine learning applications to improve performance over time. It proceeds to discuss early ANN models like the perceptron and limitations, before introducing multi-layered networks and backpropagation training. Finally, it briefly introduces self-organizing maps that can learn without supervision.
This document introduces artificial neural networks and their relationship to biological neural networks. It discusses the basic components and functioning of artificial neural networks, including nodes, links, weights, and learning. Different network architectures are described, including single layer feedforward networks and multilayer feedforward networks. Supervised, unsupervised, and reinforced learning methods are also summarized. Applications of artificial neural networks include areas like airline security, investment management, and sales forecasting.
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.
Artificial Neural Network seminar presentation using ppt.Mohd Faiz
- Artificial neural networks are inspired by biological neural networks and learning processes. They attempt to mimic the workings of the brain using simple units called artificial neurons that are connected in networks.
- Learning in neural networks involves modifying the synaptic strengths between neurons through mathematical optimization techniques. The goal is to minimize an error function that measures how well the network can approximate or complete a task.
- Neural networks can learn complex nonlinear functions through training algorithms like backpropagation that determine how to adjust the synaptic weights to improve performance on the learning task.
Artificial neural networks are computer programs that can recognize patterns in data and produce models to represent that data. They are inspired by the human brain in how knowledge is acquired through learning and stored in the connections between neurons. Neural networks learn by adjusting the strengths of connections between neurons based on examples provided during training. They are able to model and learn both linear and nonlinear relationships in data.
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.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
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.
This document provides an overview of artificial neural networks (ANNs). It begins by defining ANNs as models inspired by biological neural networks in the brain that are used to estimate functions. It then describes how biological neural networks operate in the brain with interconnected neurons. The document outlines several key properties of ANNs including plasticity, learning from experience, and their use in machine learning applications to improve performance over time. It proceeds to discuss early ANN models like the perceptron and limitations, before introducing multi-layered networks and backpropagation training. Finally, it briefly introduces self-organizing maps that can learn without supervision.
This document introduces artificial neural networks and their relationship to biological neural networks. It discusses the basic components and functioning of artificial neural networks, including nodes, links, weights, and learning. Different network architectures are described, including single layer feedforward networks and multilayer feedforward networks. Supervised, unsupervised, and reinforced learning methods are also summarized. Applications of artificial neural networks include areas like airline security, investment management, and sales forecasting.
ARTIFICIAL NEURAL NETWORKS
Simple computational elements forming a large network
Emphasis on learning (pattern recognition)
Local computation (neurons)
Configured for a particular application
Pattern recognition/data classification
ANN algorithm
Modeled after brain
Fundamental, An Introduction to Neural NetworksNelson Piedra
This document provides an introduction to neural networks. It discusses how the first wave of interest emerged after McCullock and Pitts introduced simplified neuron models in 1943. However, perceptron models were shown to have deficiencies in 1969, leading to reduced funding and many researchers leaving the field. Interest re-emerged in the early 1980s after important theoretical results like backpropagation and new hardware increased processing capacities. The document then describes key components of artificial neural networks, including processing units that receive inputs and propagate outputs, different types of connections between units, and activation and output rules. It also covers different network topologies like feed-forward and recurrent networks.
Artificial neural network model & hidden layers in multilayer artificial neur...Muhammad Ishaq
Artificial neural networks (ANNs) are computational models inspired by biological neural networks. ANNs can process large amounts of inputs to learn from data in a way similar to the human brain. There are different types of ANN architectures including single layer feedforward networks, multilayer feedforward networks, and recurrent networks. ANNs use supervised, unsupervised, or reinforced learning. The backpropagation algorithm is commonly used for training multilayer networks by propagating errors backwards from the output to adjust weights. Developing an ANN application involves collecting data, separating it into training and testing sets, designing the network architecture, initializing parameters/weights, transforming data, training the network using an algorithm like backpropagation, testing performance on new data, and
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.
This document provides an introduction to artificial neural networks (ANNs). It defines ANNs as systems inspired by the human brain that are composed of interconnected nodes that can learn relationships from large amounts of data. The document outlines the key components of ANNs, including artificial neurons, weights, biases, and activation functions. It also discusses how ANNs are trained, their advantages like parallel processing and fault tolerance, and applications in areas like pattern recognition, speech recognition, and medical diagnosis. Finally, it acknowledges some disadvantages of ANNs and discusses future areas of development like self-driving cars.
Artificial neural networks and its applicationHưng Đặng
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
Artificial neural networks seminar presentation using MSWord.Mohd Faiz
This document provides an overview of artificial neural networks. It discusses neural network architectures including feedforward and recurrent networks. It covers neural network learning methods such as supervised learning, unsupervised learning, and reinforcement learning. Backpropagation is described as a method for training neural networks by calculating partial derivatives of the error function. Higher order learning algorithms and considerations for designing neural networks like choosing the number of hidden layers and activation functions are also summarized.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs consist of interconnected nodes that operate in parallel to solve problems. The document discusses ANN components like neurons and weights, compares ANNs to biological neural networks, and outlines ANN architectures, learning methods, applications, and more. It provides an overview of ANNs and their relationship to the human brain.
The document provides an introduction to neural networks, including:
- Biological neural networks transmit signals via neurons connected by synapses and axons.
- Artificial neural networks are composed of simple processing elements (neurons) that operate in parallel and are determined by network structure and connection strengths (weights).
- Multilayer neural networks consist of an input layer, hidden layers, and output layer connected by weights to solve complex problems. Learning involves updating weights so the network can efficiently perform tasks.
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
This document provides an overview of artificial neural networks (ANNs). It defines ANNs as systems loosely modeled after the human brain that are able to learn from experience to improve performance. ANNs can be used for functions like classification, clustering, prediction, and function approximation. The document discusses the basic structure of biological neurons and ANNs, including different connection types, topologies, and learning methods. It also compares key similarities and differences between computers and the human brain.
The document summarizes key aspects of artificial neural networks and supervised learning. It discusses how biological neural networks inspired the development of artificial neural networks. The basic neuron model and perceptron are introduced as simple computing elements. Multilayer neural networks are presented as able to learn complex patterns through backpropagation algorithms that reduce errors by adjusting weights between layers.
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.
The document discusses artificial neural networks (ANNs). It describes ANNs as computing systems composed of interconnected processing elements that mimic the human brain. ANNs can solve complex problems in parallel and are fault tolerant. The key components of an ANN are the input, hidden and output layers. Feedforward and feedback networks are described. Backpropagation is used to train ANNs by adjusting weights and biases based on error. Training can be supervised, unsupervised or reinforced learning. Patterns and batch modes of training are also outlined.
This document provides an overview of neural networks. It discusses that artificial neural networks (ANNs) are computational models inspired by the human nervous system. ANNs are composed of interconnected processing units (neurons) that learn by example. There are typically three layers in a neural network: an input layer, hidden layers that process inputs, and an output layer. Neural networks can learn complex patterns and are used for applications like pattern recognition. The document also describes how biological neurons function and the key components of artificial neurons and neural network models. It explains different learning methods for neural networks including supervised, unsupervised, and reinforcement learning.
This document provides an overview of the main structures and functions of the brain and nervous system. It discusses the four lobes of the brain and their functions, as well as the limbic system and its role in emotions. It also describes the basic anatomy and cell types of the brain and spinal cord, including neurons, glial cells, grey matter, and white matter. Key functions such as processing sensory information and memory are highlighted.
ARTIFICIAL NEURAL NETWORKS
Simple computational elements forming a large network
Emphasis on learning (pattern recognition)
Local computation (neurons)
Configured for a particular application
Pattern recognition/data classification
ANN algorithm
Modeled after brain
Fundamental, An Introduction to Neural NetworksNelson Piedra
This document provides an introduction to neural networks. It discusses how the first wave of interest emerged after McCullock and Pitts introduced simplified neuron models in 1943. However, perceptron models were shown to have deficiencies in 1969, leading to reduced funding and many researchers leaving the field. Interest re-emerged in the early 1980s after important theoretical results like backpropagation and new hardware increased processing capacities. The document then describes key components of artificial neural networks, including processing units that receive inputs and propagate outputs, different types of connections between units, and activation and output rules. It also covers different network topologies like feed-forward and recurrent networks.
Artificial neural network model & hidden layers in multilayer artificial neur...Muhammad Ishaq
Artificial neural networks (ANNs) are computational models inspired by biological neural networks. ANNs can process large amounts of inputs to learn from data in a way similar to the human brain. There are different types of ANN architectures including single layer feedforward networks, multilayer feedforward networks, and recurrent networks. ANNs use supervised, unsupervised, or reinforced learning. The backpropagation algorithm is commonly used for training multilayer networks by propagating errors backwards from the output to adjust weights. Developing an ANN application involves collecting data, separating it into training and testing sets, designing the network architecture, initializing parameters/weights, transforming data, training the network using an algorithm like backpropagation, testing performance on new data, and
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.
This document provides an introduction to artificial neural networks (ANNs). It defines ANNs as systems inspired by the human brain that are composed of interconnected nodes that can learn relationships from large amounts of data. The document outlines the key components of ANNs, including artificial neurons, weights, biases, and activation functions. It also discusses how ANNs are trained, their advantages like parallel processing and fault tolerance, and applications in areas like pattern recognition, speech recognition, and medical diagnosis. Finally, it acknowledges some disadvantages of ANNs and discusses future areas of development like self-driving cars.
Artificial neural networks and its applicationHưng Đặng
Artificial neural networks (ANNs) are non-linear data driven approaches that can identify patterns in complex data. ANNs imitate the human brain in learning from examples rather than being explicitly programmed. There are various types of ANN architectures, but feedforward and recurrent networks are most common. ANNs have been successfully applied to problems in diverse domains, including classification, prediction, and modeling where relationships are unknown. Developing an effective ANN model requires selecting variables, dividing data into training/testing/validation sets, determining network architecture, evaluating performance, and training the network through iterative adjustment of weights.
Artificial Neural Network Paper Presentationguestac67362
The document provides an introduction to artificial neural networks. It discusses how neural networks are designed to mimic the human brain by using interconnected processing elements like neurons. The key aspects covered are:
- Neural networks can perform tasks like pattern recognition that are difficult for traditional algorithms.
- They are composed of interconnected nodes that transmit scalar messages to each other via weighted connections like synapses.
- Neural networks are trained by presenting examples, allowing the weighted connections to adjust until the network produces the desired output for each input.
Artificial neural networks seminar presentation using MSWord.Mohd Faiz
This document provides an overview of artificial neural networks. It discusses neural network architectures including feedforward and recurrent networks. It covers neural network learning methods such as supervised learning, unsupervised learning, and reinforcement learning. Backpropagation is described as a method for training neural networks by calculating partial derivatives of the error function. Higher order learning algorithms and considerations for designing neural networks like choosing the number of hidden layers and activation functions are also summarized.
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Artificial neural networks (ANNs) are computing systems inspired by biological neural networks. ANNs consist of interconnected nodes that operate in parallel to solve problems. The document discusses ANN components like neurons and weights, compares ANNs to biological neural networks, and outlines ANN architectures, learning methods, applications, and more. It provides an overview of ANNs and their relationship to the human brain.
The document provides an introduction to neural networks, including:
- Biological neural networks transmit signals via neurons connected by synapses and axons.
- Artificial neural networks are composed of simple processing elements (neurons) that operate in parallel and are determined by network structure and connection strengths (weights).
- Multilayer neural networks consist of an input layer, hidden layers, and output layer connected by weights to solve complex problems. Learning involves updating weights so the network can efficiently perform tasks.
- The document introduces artificial neural networks, which aim to mimic the structure and functions of the human brain.
- It describes the basic components of artificial neurons and how they are modeled after biological neurons. It also explains different types of neural network architectures.
- The document discusses supervised and unsupervised learning in neural networks. It provides details on the backpropagation algorithm, a commonly used method for training multilayer feedforward neural networks using gradient descent.
This document provides an overview of artificial neural networks (ANNs). It defines ANNs as systems loosely modeled after the human brain that are able to learn from experience to improve performance. ANNs can be used for functions like classification, clustering, prediction, and function approximation. The document discusses the basic structure of biological neurons and ANNs, including different connection types, topologies, and learning methods. It also compares key similarities and differences between computers and the human brain.
The document summarizes key aspects of artificial neural networks and supervised learning. It discusses how biological neural networks inspired the development of artificial neural networks. The basic neuron model and perceptron are introduced as simple computing elements. Multilayer neural networks are presented as able to learn complex patterns through backpropagation algorithms that reduce errors by adjusting weights between layers.
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.
The document discusses artificial neural networks (ANNs). It describes ANNs as computing systems composed of interconnected processing elements that mimic the human brain. ANNs can solve complex problems in parallel and are fault tolerant. The key components of an ANN are the input, hidden and output layers. Feedforward and feedback networks are described. Backpropagation is used to train ANNs by adjusting weights and biases based on error. Training can be supervised, unsupervised or reinforced learning. Patterns and batch modes of training are also outlined.
This document provides an overview of neural networks. It discusses that artificial neural networks (ANNs) are computational models inspired by the human nervous system. ANNs are composed of interconnected processing units (neurons) that learn by example. There are typically three layers in a neural network: an input layer, hidden layers that process inputs, and an output layer. Neural networks can learn complex patterns and are used for applications like pattern recognition. The document also describes how biological neurons function and the key components of artificial neurons and neural network models. It explains different learning methods for neural networks including supervised, unsupervised, and reinforcement learning.
This document provides an overview of the main structures and functions of the brain and nervous system. It discusses the four lobes of the brain and their functions, as well as the limbic system and its role in emotions. It also describes the basic anatomy and cell types of the brain and spinal cord, including neurons, glial cells, grey matter, and white matter. Key functions such as processing sensory information and memory are highlighted.
Use of artificial neural network in pattern recognitionkamalsrit
This document summarizes research on using artificial neural networks for pattern recognition. It discusses how pattern recognition involves tasks like classification and clustering. It describes the main stages of a pattern recognition system as data acquisition, representation, and decision making. It then focuses on artificial neural networks, describing their ability to learn complex relationships and adapt to data. Finally, it briefly mentions one previous work from 2009 that used neural networks for voice pattern recognition in interactive voice response systems.
This document defines and describes pattern recognition. It discusses what patterns are, pattern recognition systems, the pattern recognition procedure, approaches to pattern recognition including statistical, syntactic/structural, and neural network approaches. It also covers the components of a pattern recognition system including sensing, segmentation/grouping, feature extraction, classification, and post-processing. Finally, it discusses the design cycle for pattern recognition systems including data collection, feature/model choice, training, evaluation, and computational complexity.
This document provides an overview of associative memories and discrete Hopfield networks. It begins with introductions to basic concepts like autoassociative and heteroassociative memory. It then describes linear associative memory, which uses a Hebbian learning rule to form associations between input-output patterns. Next, it covers Hopfield's autoassociative memory, a recurrent neural network for associating patterns to themselves. Finally, it discusses performance analysis of recurrent autoassociative memories. The document presents key concepts in associative memory theory and different models like linear associative memory and Hopfield networks.
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.
A brief introduction to Pattern Recognition. Slides were used for a Seminar at the Interactive Art PhD at School of Arts of the UCP, Porto, Portugal (http://artes.ucp.pt)
This document summarizes artificial neural networks. It discusses how neural networks are composed of interconnected neurons that can learn complex behaviors through simple principles. Neural networks can be used for applications like pattern recognition, noise reduction, and prediction. The key components of neural networks are neurons, synapses, weights, thresholds, and activation functions. Neural networks offer advantages like adaptability and fault tolerance, though they are not exact and can be complex. Examples of neural network applications discussed include object trajectory learning, radiosity for virtual reality, speechreading, target detection and tracking, and robotics.
The document proposes a new method for detecting leakage current in bipolar junction transistors. It describes how leakage current increases over time as transistors age and degrade. It then presents a testing prototype that measures the voltage drop across a transistor under test to calculate its leakage current. The prototype applies a 300V supply to the transistor through a 1kΩ resistor. It then measures the voltage and uses Ohm's law to calculate the leakage current in μA. Testing several transistors showed some with negligible leakage current differences, while others exceeded the maximum permissible 1μA leakage, identifying them as faulty. The method provides an easy way to measure transistor leakage current for quality control.
Aula 02 - Seminário Sobre a Igreja (Segunda Temporada)IBC de Jacarepaguá
Martinho Lutero afirmou que "Quem quer encontrar Cristo deve primeiro encontrar a igreja". O documento discute se esta afirmação ainda é válida nos dias de hoje e conclui que sim, tendo respaldo bíblico. A igreja tem grandes responsabilidades de servir, exercer a misericórdia e cumprir sua missão de pregar o evangelho, o que requer compromisso e profundidade por parte dos crentes.
Fararos is a leading global IT company present in 65 countries across 4 continents that deals in mobile apps, software solutions, database management systems, and B2B markets. To attract and retain top talent, Fararos provides unmatched employee benefits like free food, medical insurance, company cars, and unique amenities such as a limousine service, aquarium gym, and extreme sports facility. The company also rewards its best performers each year with an all-expenses-paid vacation to anywhere in the world to recognize their hard work and allow them to recharge from their busy work lives.
Teacher's Kit for Interactive Journalism by Juliana Ruhfusjulianaruhfus
Learn how to conduct an interactive investigation on environmental crimes. Recommended for journalism educators interested in fact-checking and verification practices.
Based on the Pirate Fishing interactive investigation by Juliana Ruhfus at http://www.aljazeera.com/piratefishing
Este documento explica los conceptos básicos de los logaritmos. Define los logaritmos como el exponente al que se debe elevar la base para obtener el número. Cubre los logaritmos decimales (base 10), los logaritmos neperianos o naturales (base e), y algunas propiedades clave como que el logaritmo de un producto es la suma de los logaritmos de los factores y el logaritmo de una potencia es el producto del exponente por el logaritmo de la base. También incluye ejemplos numéricos
Adapted physical education is physical education that has been modified for students with disabilities so they can participate as fully as their non-disabled peers. It involves developing motor skills, physical skills, and skills in sports and activities. Students with disabilities like learning disabilities, ADHD, or hearing impairments may need instruction tailored to their specific needs, like oral instructions, immediate feedback, multi-sensory teaching, or visual aids. Adapted physical education aims to make physical activity accessible to all students.
Seminário: "SOBRE A IGREJA..."
Ministrado durante a E.B.D. da Igreja Batista Central de Jacarepaguá, pelo Pr. Júlio César.
Mais informações no Site: http://www.ibcjrj.com.br
Este documento es un proyecto final en español 1 donde el estudiante Xzavian Carter responde preguntas básicas sobre sí mismo en español, incluyendo su nombre, cómo está, la fecha, de dónde es, su nacionalidad, cuántos años tiene y su cumpleaños, qué le gusta y no le gusta hacer, e imágenes relacionadas a cada respuesta. También incluye preguntas dirigidas a su maestra.
JAISTサマースクール2016「脳を知るための理論」講義04 Neural Networks and Neuroscience hirokazutanaka
This document summarizes key concepts from a lecture on neural networks and neuroscience:
- Single-layer neural networks like perceptrons can only learn linearly separable patterns, while multi-layer networks can approximate any function. Backpropagation enables training multi-layer networks.
- Recurrent neural networks incorporate memory through recurrent connections between units. Backpropagation through time extends backpropagation to train recurrent networks.
- The cerebellum functions similarly to a perceptron for motor learning and control. Its feedforward circuitry from mossy fibers to Purkinje cells maps to the layers of a perceptron.
This document provides an overview of neural networks and fuzzy systems. It outlines a course on the topic, which is divided into two parts: neural networks and fuzzy systems. For neural networks, it covers fundamental concepts of artificial neural networks including single and multi-layer feedforward networks, feedback networks, and unsupervised learning. It also discusses the biological neuron, typical neural network architectures, learning techniques such as backpropagation, and applications of neural networks. Popular activation functions like sigmoid, tanh, and ReLU are also explained.
An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.
The document provides an introduction to artificial neural networks (ANN). It defines ANNs as systems inspired by biological neural networks that consist of interconnected processing units called neurons. The document outlines the key components of ANNs, including the artificial neuron model, different network architectures, learning strategies, and activation functions. It then discusses applications of ANNs such as pattern recognition, classification, data mapping, and medical diagnosis. In closing, the document notes advantages of ANNs like their ability to model non-linear relationships and adapt to new information.
Modeling Electronic Health Records with Recurrent Neural NetworksJosh Patterson
Time series data is increasingly ubiquitous. This trend is especially obvious in health and wellness, with both the adoption of electronic health record (EHR) systems in hospitals and clinics and the proliferation of wearable sensors. In 2009, intensive care units in the United States treated nearly 55,000 patients per day, generating digital-health databases containing millions of individual measurements, most of those forming time series. In the first quarter of 2015 alone, over 11 million health-related wearables were shipped by vendors. Recording hundreds of measurements per day per user, these devices are fueling a health time series data explosion. As a result, we will need ever more sophisticated tools to unlock the true value of this data to improve the lives of patients worldwide.
Deep learning, specifically with recurrent neural networks (RNNs), has emerged as a central tool in a variety of complex temporal-modeling problems, such as speech recognition. However, RNNs are also among the most challenging models to work with, particularly outside the domains where they are widely applied. Josh Patterson, David Kale, and Zachary Lipton bring the open source deep learning library DL4J to bear on the challenge of analyzing clinical time series using RNNs. DL4J provides a reliable, efficient implementation of many deep learning models embedded within an enterprise-ready open source data ecosystem (e.g., Hadoop and Spark), making it well suited to complex clinical data. Josh, David, and Zachary offer an overview of deep learning and RNNs and explain how they are implemented in DL4J. They then demonstrate a workflow example that uses a pipeline based on DL4J and Canova to prepare publicly available clinical data from PhysioNet and apply the DL4J RNN.
- Researchers used a hierarchical convolutional neural network (CNN) optimized for object categorization performance to predict neural responses in higher visual cortex.
- The top layer of the CNN accurately predicted responses in inferior temporal (IT) cortex, and intermediate layers predicted responses in V4 cortex.
- This suggests that biological performance optimization directly shaped neural mechanisms in visual processing areas, as the CNN was not explicitly trained on neural data but emerged as predictive of responses in IT and V4.
This document provides an overview of artificial neural networks and their application as a model of the human brain. It discusses the biological neuron, different types of neural networks including feedforward, feedback, time delay, and recurrent networks. It also covers topics like learning in perceptrons, training algorithms, applications of neural networks, and references key concepts like connectionism, associative memory, and massive parallelism in the brain.
1. A perceptron is a basic artificial neural network that can learn linearly separable patterns. It takes weighted inputs, applies an activation function, and outputs a single binary value.
2. Multilayer perceptrons can learn non-linear patterns by using multiple layers of perceptrons with weighted connections between them. They were developed to overcome limitations of single-layer perceptrons.
3. Perceptrons are trained using an error-correction learning rule called the delta rule or the least mean squares algorithm. Weights are adjusted to minimize the error between the actual and target outputs.
The document provides an overview of artificial neural networks and biological neural networks. It discusses the components and functions of the human nervous system including the central nervous system made up of the brain and spinal cord, as well as the peripheral nervous system. The four main parts of the brain - cerebrum, cerebellum, diencephalon, and brainstem - are described along with their roles in processing sensory information and controlling bodily functions. A brief history of artificial neural networks is also presented.
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Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
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Lecture artificial neural networks and pattern recognition
1. T H E UN I V E R S I T Y of TE X A S
HE A L T H S C I E N C E CE N T E R A T HO U S T O N
S C H O O L of HE A L T H I N F O R M A T I O N S C I E N C E S
Artificial Neural Networks and
Pattern Recognition
For students of HI 5323
“Image Processing”
Willy Wriggers, Ph.D.
School of Health Information Sciences
http://biomachina.org/courses/processing/13.html
4. Neuro-
Physiological
Background
• 10 billion neurons in
human cortex
• 60 trillion synapses
• In first two years from birth
~1 million synapses / sec.
formed
pyramidal cell
19. Main Types of ANN
Supervised Learning:
ƒ Feed-forward ANN
- Multi-Layer Perceptron (with sigmoid hidden neurons)
ƒ Recurrent Networks
- Neurons are connected to self and others
- Time delay of signal transfer
- Multidirectional information flow
Unsupervised Learning:
ƒ Self-organizing ANN
- Kohonen Maps
- Vector Quantization
- Neural Gas
64. Vector Quantization
Lloyd (1957)
Linde, Buzo, & Gray (1980)
Martinetz & Schulten (1993)
Digital Signal Processing,
Speech and Image Compression.
Neural Gas.
}
Encode data (in ℜ D ) using a finite set { w j } (j=1,…,k) of codebook vectors.
Delaunay triangulation divides ℜ D into k Voronoi polyhedra (“receptive fields”):
V i = { v ∈ℜ D v − w i ≤ v − w j ∀j
}
66. k-Means a.k.a. Linde, Buzo & Gray (LBG)
Encoding Distortion Error:
2
E = Σ vi −wj i d
(data points) ( ) i
i
Lower E ( { w ( t ) } ) iteratively: Gradient descent j
∀r :
w t w t w t E v w d
∂ Σ
r ( ) r ( ) r ( 1 ) rj ( i ) ( i r ) i
.
2 w
r i
ε
ε δ
∂
Δ ≡ − − = − ⋅ = ⋅ −
v (t) i : i d
Inline (Monte Carlo) approach for a sequence selected at random
according to propability density function
( ) ~ ( ( ) ) . r rj(i) i r Δw t = ε ⋅δ ⋅ v t −w
Advantage: fast, reasonable clustering.
Limitations: depends on initial random positions,
difficult to avoid getting trapped in the many local minima of E
67. Neural Gas Revisited
Avoid local minima traps of k-means by smoothing of energy function:
r
−
r r w t e v t w
∀ Δ = ⋅ ⋅ −
( ( ) { }) r i j s v t , w
s
: ( ) ~ ε λ
( i ( ) r
) , Where is the closeness rank:
v w v w v w
− ≤ − ≤ ≤ − −
s s s k
i j i j … i j k
0 1 ( 1)
= = = −
0 1 1
r r r
68. Neural Gas Revisited
λ →0 :
λ ≠ 0 : j(i ) w
({ } ) k 2
λ E w λ
= Σ e Σ vi −
i wj d , ( ) .
r 1
sr
j i
i
−
=
Note: k-means.
not only “winner” , also second, third, ... closest are updated.
Can show that this corresponds to stochastic gradient descent on
λ →0 : E~→ E .
λ → ∞ : E~ } ⇒ λ (t)
Note: k-means.
parabolic (single minimum).
69. Neural Gas Revisited
Q: How do we know that we have found the global minimum of E?
A: We don’t (in general).
{ } j w
But we can compute the statistical variability of the by repeating the
calculation with different seeds for random number generator.
Codebook vector variability arises due to:
• statistical uncertainty,
• spread of local minima.
A small variability indicates good convergence behavior.
Optimum choice of # of vectors k: variability is minimal.
90. Neural Network References
• Neural Networks, a Comprehensive Foundation, S. Haykin, ed. Prentice Hall
(1999)
• Neural Networks for Pattern Recognition, C. M. Bishop, ed Claredon Press,
Oxford (1997)
• Self Organizing Maps, T. Kohonen, Springer (2001)
91. Some ANN Toolboxes
• Free software
ƒ SNNS: Stuttgarter Neural Network Systems Java NNS
ƒ GNG at Uni Bochum
• Matlab toolboxes
ƒ Fuzzy Logic
ƒ Artificial Neural Networks
ƒ Signal Processing
92. Pattern Recognition /
Vector Quantization References
Textbooks
Duda, Heart: Pattern Classification and Scene Analysis. J. Wiley Sons, New York,
1982. (2nd edition 2000).
Fukunaga: Introduction to Statistical Pattern Recognition. Academic Press, 1990.
Bishop: Neural Networks for Pattern Recognition. Claredon Press, Oxford, 1997.
Schlesinger, Hlaváč: Ten lectures on statistical and structural pattern recognition.
Kluwer Academic Publisher, 2002.