An artificial neural network (ANN) is a computational model inspired by the human brain that can learn from large amounts of data to detect patterns and relationships. ANNs are formed from hundreds of artificial neurons connected by coefficients that are organized in layers. The power of ANNs comes from connecting neurons, with each neuron consisting of a weighted input, transfer function, and single output. ANNs learn by adjusting the weights between neurons to minimize error and reach a specified level of accuracy when trained on data. Once trained, ANNs can be used to make predictions on new input data.
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 discusses neural networks, including human neural networks and artificial neural networks (ANNs). It provides details on the key components of ANNs, such as the perceptron and backpropagation algorithm. ANNs are inspired by biological neural systems and are used for applications like pattern recognition, time series prediction, and control systems. The document also outlines some current uses of neural networks in areas like signal processing, anomaly detection, and soft sensors.
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.
An artificial neural network (ANN) is a computational model inspired by the human brain that can learn from large amounts of data to detect patterns and relationships. ANNs are formed from hundreds of artificial neurons connected by coefficients that are organized in layers. The power of ANNs comes from connecting neurons, with each neuron consisting of a weighted input, transfer function, and single output. ANNs learn by adjusting the weights between neurons to minimize error and reach a specified level of accuracy when trained on data. Once trained, ANNs can be used to make predictions on new input data.
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 discusses neural networks, including human neural networks and artificial neural networks (ANNs). It provides details on the key components of ANNs, such as the perceptron and backpropagation algorithm. ANNs are inspired by biological neural systems and are used for applications like pattern recognition, time series prediction, and control systems. The document also outlines some current uses of neural networks in areas like signal processing, anomaly detection, and soft sensors.
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.
Göğüs Kanseri Verilerinin Yapay Sinir Ağları ile Sınıflandırılması (Classific...Metin Uslu
Göğüs Kanseri Verilerinin Yapay Sinir Ağları ile Sınıflandırılması (Classification with Artificial Neural Network)
Bu çalışmada, bayanlarda yaygın olarak karşılaşılan kanser çeşidi olan göğüs kanseri verileri üzerinde ileri beslemeli yapay sinir ağları kullanılarak sınıflandırılma yapılmıştır. Esnek hesaplama tekniklerinden biri olan yapay sinir ağları, son yıllarda öngörü ve sınıflandırma problemlerinde tercih edilen bir modelleme tekniğidir. Özellikle varsayımlardan bağımsız olması ve doğrusal olmayan verilerde çalışabilmesi diğer tekniklere göre avantaj sağlayan özellikleridir. Bu nedenle sınıflandırma yöntemleri içerisinde kullanılabilecek alternatif yöntemler arasındadır.
Çalışmada, göğüs kanserine ilişkin veri setindeki bağımsız değişkenler kullanılarak sınıflandırma yapılmıştır. Göğüs kanseri veri seti 699 gözlemden oluşan 9 bağımsız, 2 bağımlı değişkene sahiptir. Bu veri seti için 594 gözlem eğitim algoritması ile ağ eğitilmiş, sonraki 105 gözlem ise tahmin edilerek sınıflandırma yapılmış ve gerçek sonuçlar ile karşılaştırılarak modelin performansı ölçülmüştür. Çalışma Matlab R2012a paket programında program kodlanarak yapılmıştır.
Yapay Sinir Ağları ile birlikte Lojistik Regresyon ve Chaid sınıflandırma algoritması da kullanılarak yöntemlerin performansları karşılaştırılmıştır. Bu sonuçlara bakıldığında denenen 3 model içerisinde sonuçların yakın olduğu görülmüştür. Yalnız YSA’ nın diğerlerine göre küçük de olsa daha başarılı bir sonuç verdiği söylenebilir. Yapay sinir ağlarının diğer modellere göre sağladığı avantajlar ile belirtilen kanser verisinde alternatif yöntemlerden biri olarak başarılı sonuçlar ürettiği görülmüştür.
This document provides an overview of neural networks. It discusses how the human brain works and how artificial neural networks are modeled after the human brain. The key components of a neural network are neurons which are connected and can be trained. Neural networks can perform tasks like pattern recognition through a learning process that adjusts the connections between neurons. The document outlines different types of neural network architectures and training methods, such as backpropagation, to configure neural networks for specific applications.
Artificial neural networks (ANNs) are processing systems inspired by biological neural networks. They consist of interconnected processing elements that dynamically change their outputs based on external inputs. While much simpler than actual brains, some ANNs have accurately modeled systems like the retina. ANNs are initially trained on large datasets to learn input-output relationships, then make predictions on new inputs. They are nonlinear, adaptable systems suited for parallel processing tasks.
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 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.
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.
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.
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
mathematical models involved are presented and
demonstrated.
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.
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 discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Deep learning is a branch of machine learning that uses neural networks with multiple processing layers to learn representations of data with multiple levels of abstraction. It has been applied to problems like image recognition, natural language processing, and game playing. Deep learning architectures like deep neural networks use techniques like pretraining, dropout, and early stopping to avoid overfitting. Popular deep learning frameworks and libraries include TensorFlow, Keras, and PyTorch.
Introduction to Neural networks (under graduate course) Lecture 7 of 9Randa Elanwar
This document provides an overview of neural network learning techniques including supervised, unsupervised, and reinforcement learning. It discusses the Hebbian learning rule, which updates weights based on the activation of connected neurons. Examples are provided to illustrate how the Hebbian rule can be used to train networks without error signals by detecting correlations in input-output patterns.
Neural networks are a type of data mining technique inspired by biological neural systems. They are composed of interconnected nodes similar to neurons in the brain. Neural networks can learn patterns from complex data through supervised or unsupervised learning methods. They are widely used for applications like fraud detection, risk assessment, image recognition, and stock market prediction due to their ability to learn from examples without being explicitly programmed.
Deep Feed Forward Neural Networks and RegularizationYan Xu
Deep feedforward networks use regularization techniques like L2/L1 regularization, dropout, batch normalization, and early stopping to reduce overfitting. They employ techniques like data augmentation to increase the size and variability of training datasets. Backpropagation allows information about the loss to flow backward through the network to efficiently compute gradients and update weights with gradient descent.
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs and applies an activation function to produce an output. ANNs can learn from examples through a process of adjusting the weights between neurons. Backpropagation is a common learning algorithm that propagates errors backward from the output to adjust weights and minimize errors. While single-layer perceptrons can only model linearly separable problems, multi-layer feedforward neural networks can handle non-linear problems using hidden layers that allow the network to learn complex patterns from data.
Yapay sinir ağları ile alakalı İnternet de bulunan belgelerden yararlanarak, yapay sinir ağlarına giriş için hazırladığım sunumum. Yapay sinir ağları ile alakalı herhangi bir bilginiz yok ise başlangıç için ideal bir kaynak. (Telif hakkı bulundurabileceği gerekçesi ile kendim hazırlamadığım resimleri kaldırdım. )
Yazilim atolyesi subat ayi toplantisindan; yapay sinir aglari ile el yazısı tanıma programina ait sunum.
Sunumu yapan : http://derindelimavi.blogspot.com/
Göğüs Kanseri Verilerinin Yapay Sinir Ağları ile Sınıflandırılması (Classific...Metin Uslu
Göğüs Kanseri Verilerinin Yapay Sinir Ağları ile Sınıflandırılması (Classification with Artificial Neural Network)
Bu çalışmada, bayanlarda yaygın olarak karşılaşılan kanser çeşidi olan göğüs kanseri verileri üzerinde ileri beslemeli yapay sinir ağları kullanılarak sınıflandırılma yapılmıştır. Esnek hesaplama tekniklerinden biri olan yapay sinir ağları, son yıllarda öngörü ve sınıflandırma problemlerinde tercih edilen bir modelleme tekniğidir. Özellikle varsayımlardan bağımsız olması ve doğrusal olmayan verilerde çalışabilmesi diğer tekniklere göre avantaj sağlayan özellikleridir. Bu nedenle sınıflandırma yöntemleri içerisinde kullanılabilecek alternatif yöntemler arasındadır.
Çalışmada, göğüs kanserine ilişkin veri setindeki bağımsız değişkenler kullanılarak sınıflandırma yapılmıştır. Göğüs kanseri veri seti 699 gözlemden oluşan 9 bağımsız, 2 bağımlı değişkene sahiptir. Bu veri seti için 594 gözlem eğitim algoritması ile ağ eğitilmiş, sonraki 105 gözlem ise tahmin edilerek sınıflandırma yapılmış ve gerçek sonuçlar ile karşılaştırılarak modelin performansı ölçülmüştür. Çalışma Matlab R2012a paket programında program kodlanarak yapılmıştır.
Yapay Sinir Ağları ile birlikte Lojistik Regresyon ve Chaid sınıflandırma algoritması da kullanılarak yöntemlerin performansları karşılaştırılmıştır. Bu sonuçlara bakıldığında denenen 3 model içerisinde sonuçların yakın olduğu görülmüştür. Yalnız YSA’ nın diğerlerine göre küçük de olsa daha başarılı bir sonuç verdiği söylenebilir. Yapay sinir ağlarının diğer modellere göre sağladığı avantajlar ile belirtilen kanser verisinde alternatif yöntemlerden biri olarak başarılı sonuçlar ürettiği görülmüştür.
This document provides an overview of neural networks. It discusses how the human brain works and how artificial neural networks are modeled after the human brain. The key components of a neural network are neurons which are connected and can be trained. Neural networks can perform tasks like pattern recognition through a learning process that adjusts the connections between neurons. The document outlines different types of neural network architectures and training methods, such as backpropagation, to configure neural networks for specific applications.
Artificial neural networks (ANNs) are processing systems inspired by biological neural networks. They consist of interconnected processing elements that dynamically change their outputs based on external inputs. While much simpler than actual brains, some ANNs have accurately modeled systems like the retina. ANNs are initially trained on large datasets to learn input-output relationships, then make predictions on new inputs. They are nonlinear, adaptable systems suited for parallel processing tasks.
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 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.
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.
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.
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
mathematical models involved are presented and
demonstrated.
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.
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 discusses the concepts of soft computing and artificial neural networks. It defines soft computing as an emerging approach to computing that parallels the human mind in dealing with uncertainty and imprecision. Soft computing consists of fuzzy logic, neural networks, and genetic algorithms. Neural networks are simplified models of biological neurons that can learn from examples to solve problems. They are composed of interconnected processing units, learn via training, and can perform tasks like pattern recognition. The document outlines the basic components and learning methods of artificial neural networks.
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
Deep learning is a branch of machine learning that uses neural networks with multiple processing layers to learn representations of data with multiple levels of abstraction. It has been applied to problems like image recognition, natural language processing, and game playing. Deep learning architectures like deep neural networks use techniques like pretraining, dropout, and early stopping to avoid overfitting. Popular deep learning frameworks and libraries include TensorFlow, Keras, and PyTorch.
Introduction to Neural networks (under graduate course) Lecture 7 of 9Randa Elanwar
This document provides an overview of neural network learning techniques including supervised, unsupervised, and reinforcement learning. It discusses the Hebbian learning rule, which updates weights based on the activation of connected neurons. Examples are provided to illustrate how the Hebbian rule can be used to train networks without error signals by detecting correlations in input-output patterns.
Neural networks are a type of data mining technique inspired by biological neural systems. They are composed of interconnected nodes similar to neurons in the brain. Neural networks can learn patterns from complex data through supervised or unsupervised learning methods. They are widely used for applications like fraud detection, risk assessment, image recognition, and stock market prediction due to their ability to learn from examples without being explicitly programmed.
Deep Feed Forward Neural Networks and RegularizationYan Xu
Deep feedforward networks use regularization techniques like L2/L1 regularization, dropout, batch normalization, and early stopping to reduce overfitting. They employ techniques like data augmentation to increase the size and variability of training datasets. Backpropagation allows information about the loss to flow backward through the network to efficiently compute gradients and update weights with gradient descent.
An artificial neural network (ANN) is a machine learning approach that models the human brain. It consists of artificial neurons that are connected in a network. Each neuron receives inputs and applies an activation function to produce an output. ANNs can learn from examples through a process of adjusting the weights between neurons. Backpropagation is a common learning algorithm that propagates errors backward from the output to adjust weights and minimize errors. While single-layer perceptrons can only model linearly separable problems, multi-layer feedforward neural networks can handle non-linear problems using hidden layers that allow the network to learn complex patterns from data.
Yapay sinir ağları ile alakalı İnternet de bulunan belgelerden yararlanarak, yapay sinir ağlarına giriş için hazırladığım sunumum. Yapay sinir ağları ile alakalı herhangi bir bilginiz yok ise başlangıç için ideal bir kaynak. (Telif hakkı bulundurabileceği gerekçesi ile kendim hazırlamadığım resimleri kaldırdım. )
Yazilim atolyesi subat ayi toplantisindan; yapay sinir aglari ile el yazısı tanıma programina ait sunum.
Sunumu yapan : http://derindelimavi.blogspot.com/
The document discusses data mining and presents an overview of key concepts. It defines data mining as the process of discovering interesting patterns from large amounts of data. It outlines the typical steps in a data mining process, including data cleaning, integration, selection, transformation, mining, evaluation, and presentation. It also describes common data mining functionalities like characterization, discrimination, association, classification, clustering, and outlier analysis. Finally, it lists some references for further reading on data mining.
Introduction VU University Amsterdam master tscm december 2012sdeleeuw
The document summarizes a Master's program in Transport and Supply Chain Management (TSCM) at a university. The program focuses on designing and organizing flows of goods, services, information, and money in supply chains and transportation networks. Coursework covers topics like distribution logistics, transport economics, decision-making, and network analysis. Students complete practical projects and a thesis. Graduates obtain jobs in supply chain management, logistics consulting, transportation coordination, and more, at companies like IBM, Philips, DHL, and KLM.
This document describes how genetic algorithms work through an example. It shows three iterations of a genetic algorithm optimizing a function y=x1-x2, where x1 and x2 are integers between 0-7 and 0-3 respectively. In each iteration, the algorithm calculates the y value for different combinations of x1 and x2. The best solutions are used to generate the values for the next iteration, getting closer to the highest y value each time.
Yapay Sinir Ağı Geliştirmesi ve Karakter TanımaBusra Pamuk
Lisans bitirme projesi olarak hazırlamış olduğum projenin rapor dosyasıdır. Projemde kısaca; ileri beslemeli yapay sinir ağı modelini ve öğrenme işlemini gerçekleştirmek için geri besleme algoritmasını Java programlama dilini kullanarak implement ettim. Oluşturduğum yapay sinir ağını bir arayüz ile birleştirerek harf tanıma yapan bir sistem oluşturdum.
Yapay Sinir Ağı Geliştirmesi ve Karakter TanımaBusra Pamuk
Lisans bitirme projesi olarak hazırlamış olduğum projenin sunum dosyasıdır. Projemde kısaca; ileri beslemeli yapay sinir ağı modelini ve öğrenme işlemini gerçekleştirmek için geri besleme algoritmasını Java programlama dilini kullanarak implement ettim. Oluşturduğum yapay sinir ağını bir arayüz ile birleştirerek harf tanıma yapan bir sistem oluşturdum.
The document provides product information about the Music Speaker E15 Bluetooth speaker from Aiptek International GmbH. It can connect to smartphones, tablets, and notebooks via Bluetooth or NFC to stream high-quality 360° sound. It has built-in rechargeable batteries allowing up to 5 hours of music playback and can be wirelessly charged using the included inductive charging station.
This document compares the key features and specifications of three action cameras: the Aiptek SportyCam Z3, GoPro HD Hero2, and Jay Tech DV123. The GoPro is the most expensive at $349 but offers full HD video at 1080p/30fps and the highest resolution photos and sensor. The Aiptek is cheaper at $199 and also shoots full HD video, but has a smaller LCD and requires additional purchases for accessories. The Jay Tech is the least expensive at $149 but only shoots HD 720p video and has a smaller sensor and battery.
Air2u is a business unit of Aiptek International that produces the Mobile Eyes HD, a portable Wi-Fi camera for home security and live video broadcasting. The Mobile Eyes HD has a 1 megapixel CMOS sensor, records 720p HD video at 30 fps, supports up to 64GB of additional storage, and has a 100 degree wide viewing angle. It connects to smartphones and PCs via an app for live monitoring and two-way audio communication over WiFi, and includes accessories like wall and tripod mounts.
The document provides instructions for modeling a camel in 3D using a polygon cube as a starting shape. The steps include inserting edge loops, extruding faces to add details like feet and fingers, merging vertex points to smooth shapes, and duplicating and merging parts to construct the full camel body. Modeling progresses from basic cube to defined camel form through selective editing of vertices, edges, and faces.
Zurafa es una ciudad en Argelia. Se encuentra en el norte del país, cerca de la costa mediterránea. Es conocida por su arquitectura colonial francesa y por ser un centro de comercio y transporte en la región.
2. Giriş
Yapay sinir ağları, insan beyni ile benzer şekilde çalışırlar. Deneyimler
yoluyla öğrenirler ve karmaşık hesaplamalara dayanan sorunlara çözüm
getirirler. Bir topluluk içinden belli bir yüzü nasıl seçeriz? Yada bir
uçağın iniş rotasını tahmin edip, hatalara nasıl daha oluşmadan
müdahale edebiliriz? İşte bu tip durumlarda, insan beyni “sinir
hücreleri” adı verilen birbirine bağlı bir grup işlem birimi kullanır. Her
sinir hücresi bağımsızdır. Her biri kendi işini görür, diğer birimlerle
eşzamanlı olarak çalışma ihtiyacı duymaz.
Öncelikle, karşılaşılan problemler tek ve basit bir algoritma kullanılarak
çözülebilecek türden değildir. Ayrıca kullanılan veriler de genellikle
pürüzsüz yada tam değildir. Örneğin, bir yüz tanıma uygulamasına veri
olarak girilen resimlerde kişilerin gözlüklü yada şapkalı resimleri yoksa,
veri kümesi eksik demektir.
3. Sinir Ağı:
Basit yada karmaşık birçok sinir ağı çeşidi vardır.
Çıktı
Girdi
Örneğin, katmanlardan oluşan ileri
beslemeli bir sinir ağında her katman
işlem birimleri, yani sinir
hücrelerinden oluşur. Katmanlar
aldıkları veriler üzerinde bağımsız
hesaplamalar yapar ve sonuçları bir
sonraki katmana iletir. Son katman ise
ağın çıktısını belirler.
4. Bir Yapay Sinir Hücresinin Çıktısı
Temel olarak, bir yapay sinir hücresinin iç aktivasyonu yada ham
çıktısı, girdilerin ağırlıklandırılmış toplamıdır. Ancak genelde son
değeri belirlemek için bir eşik fonksiyonu da kullanılır. Çıktı 1
ise, sinir hücresi harekete geçer (aktif hale gelir). Çıktı 0 ise, sinir
hücresi harekete geçmez.
5. Yapay Sinir Ağları Nasıl Öğrenir?
Yapay sinir hücrelerini birbirine bağlayan bağlantıların her birinin
sayısal bir ağırlığı vardır. Yapay sinir ağlarında bu ağırlıklar, uzun
dönemlik hafızaya karşılık gelir. Bir yapay sinir ağı bu ağırlıkların
tekrar tekrar ayarlanması sayesinde öğrenir.
6. Basit bir sinir hücresinin işleyişi şu
şekildedir
1-) Giriş sinyallerinin ağırlıklandırılmış toplamı alınır.
2-) Bir eşik değeri seçilir.
3-) Girdilerin toplamı eşik değeri ile karşılaştırılır. Bu değer eşik
değerinden küçükse çıktı -1, büyükse +1 olur. (ikinci durumda sinir
hücresi aktifleşmiştir.)
7. YAPAY SİNİR AĞI TÜRLERİ
Tek Katmanlı Sinir Ağları:
Tek katmanlı yapay sinir ağları sadece girdi ve çıktı
katmanlarından oluşur. En basit tek katmanlı sinir ağı modeli
perseptron’dur.
8. Perseptron
Perseptron modeli eğitilebilen
tek bir yapay sinir hücresinden
oluşur. Bu modelde
ağırlıklandırılmış girdiler
aktivasyon fonksiyonuna
uygulanır ve +1 yada -1
şeklinde bir çıktı alınır.
Perseptron’un amacı girdileri
sınıflandırmaktır. Bu demektir
ki n-boyutlu uzay şekildeki gibi
bir doğru yada düzlem ile iki
bölgeye ayrılır.
9. Çok Katmanlı Sinir Ağları
Çok katmanlı sinir ağları bir ya da daha fazla gizli katmanı olan ileri
beslemeli sinir ağlarıdır. Genelde bu ağ bir girdi katmanı, en az bir
gizli katman ve bir de çıkış katmanından oluşur.
Gizli katmanlara ihtiyaç duyulmasının sebebi girdi katmanlarından
gelen genellikle işlenmemiş sinyallerin özelliklerini belirlemek,
ağırlıklandırmak ve sonuçları çıktı katmanına yönlendirmektir.
11. Hopfield Ağı
Hopfield modeli, her biri birbirine bağlı sinir hücrelerinden oluşan bir
kümeyi içerir. Girdi ve çıktı hücreleri arasında fark gözetilmez.
Tür - Geri Besleme
Katmanlar - Tek Matris
Girdi Değeri
Türleri
- İkili (Binary)
Aktivasyon
Fonksiyonu
- İşaret
Öğrenme Yöntemi - Denetsiz
Öğrenme
Algoritması
- Delta Öğrenme Kuralı (EK-1)
- Benzetimli Tavlama (EK-1)
Kullanım Alanları - Desen İlişkileri
- Eniyileme (Optimizasyon) Problemleri
12. Kohonen Özellik Haritası
İnsan beyninin taklit edilmesi konusunda en kullanışlı sinir ağı türü olarak
kabul edilebilir. Bu türün kalbi, sinir hücrelerinin kendilerini belirli girdi
değerlerine göre düzenledikleri bir katman olan özellik haritasıdır.
Tür - Geri Besleme / İleri Besleme
Katmanlar - 1 Girdi Katmanı, 1 Harita Katmanı
Girdi Değeri
Türleri
- İkili (Binary), Reel (Real)
Aktivasyon
Fonksiyonu
- Sigmoid
Öğrenme Yöntemi - Denetsiz
Öğrenme
Algoritması
- Öz-Örgütleme
Kullanım Alanları - Desen Sınıflandırma
- Eniyileme (Optimizasyon) Problemleri
- Benzetim
13. BAZI ÖĞRENME ALGORİTMALARI
Bir sinir ağının öğrenme algoritması ‘denetli’ ya da ‘denetsiz’ olabilir.
İstenilen çıktısı önceden bilinen sinir ağına ‘denetli’ sinir ağı denir.
İleri Yayılım
İleri yayılım, denetli bir öğrenme algoritmasıdır ve bir sinir ağının
girdi katmanından çıktı katmanına doğru ‘bilgi akışı’nı açıklar.
14. Geri Yayılım
Geri yayılım, genellikle çok katmanlı perseptronların, ağın gizli
katmanlarına bağlı olan ağırlıklarını değiştirmek için kullanılan
denetli bir öğrenme algoritmasıdır.
Geri yayılım algoritması ağırlıkları ters yönde değiştirmek için
hesaplanmış hata değerleri kullanır. Bu hatayı elde etmek için
öncelikle 1 ileri yayılım safhası tamamlanmalıdır. İleri doğru
yayılırken, sinir hücreleri sigmoid fonksiyonu kullanılarak
etkinleştirilir
15. Öz-Örgütlenme
Öz-Örgütlenme, Kohonen özellik haritaları tarafından
kullanılan denetsiz bir öğrenme algoritmasıdır. Genel olarak
bilindiği gibi insan beyninin korteksi, her biri farklı işlevlere
sahip bölgelere ayrılmıştır. Sinir hücreleri gelen bilgilere göre
kendilerini gruplandırmıştır. Gelen bilgiler tek bir sinir hücresi
tarafından alınmazlar, çevre hücreler de bu bilgiyi bir şekilde
alır. Sonuç olarak bu örgütlenme bir çeşit harita yaratır.
Biyolojik sinir hücrelerinin bu yapısı yapay sinir ağlarında
‘Kohonen Özellik Haritası’ kullanılarak taklit edilebilir.
16. Görüldüğü üzere girdi katmanındaki her
sinir hücresi, haritadaki diğer bütün sinir
hücreleri ile bağlantılıdır. Sonuçta ortaya
çıkan ağırlık matrisi ağın girdi değerlerini
haritadaki sinir hücrelerine aktarmak için
kullanılır.
Ayrıca haritadaki bütün sinir hücreleri de
kendi aralarında bağlantılıdır. Bu
bağlantılar, aktivasyonun belirli bir
bölgesindeki sinir hücrelerini, en büyük
aktivasyona sahip sinir hücresi etrafında
toplanmaya teşvik eder.
17. SOM
SOM ağları, Teuvo Kohonen tarafından geliştirilmiştir. Genel
olarak sınıflandırma yapmak için kullanılmaktadır. Bu ağların en
temel özelliği olayları öğrenmek için bir öğretmene ihtiyaç
duymamasıdır (denetsiz). İleri besleme/geri besleme türünde
olabilir ve öğrenme algoritması olarak öz-örgütlenme (“Bazı
Öğrenme Algoritmaları” başlığı altında anlatılmıştır) yöntemini
kullanır. Bir girdi katmanı ve bir harita katmanı bulunur.
Öz-örgütlenme kullanan Kohonen Özellik Haritaları insan
beynini taklit edecek şekilde tasarlanmıştır. İnsan beyninde
öğrenme işlemi, sürekli tekrarlanan olaylar ve durumlar
karşısında beyine iletilen sinyallerin, korteksin belli bölgelerinde
yoğunlaşması sonucu bir hafıza oluşması şeklinde gerçekleşir.
18. Benzer şekilde SOM ağlarına gönderilen sinyaller (girdi değerleri)
bazı işlemlerden geçerek (iletilme-ağırlıklandırma) harita katmanına
ulaşır. Bu katman 1 yada 2 boyutlu olarak dizilmiş sinir
hücrelerinden oluşmaktadır.
Korteks görevini yapan bu katmana gönderilen girdiler, yapılan
matematiksel hesaplamalar sonucu bir bölgede yoğunlaşırlar. Bu
bölge; matematiksel işlemlerle belirlenen “kazanan sinir hücresi”dir.
Bu sinir hücresine ait bir alan mevcuttur. Aktivasyon alanı olarak
adlandırılan bu bölge öğrenme esnasında küçülür. Bu küçülme,
örneğin sınıflandırma işlemlerinde kesinliğin artmasına karşılık
gelir. Her sınıf için ayrı bir sinir hücresi etrafındaki toplanmalar,
sonuçta sınıflara ait bölgeler oluşturur. Bu şekilde de sınıflara ait
öğeler daha sonra kolaylıkla tespit edilir.
19.
20.
21. 1. adımda yapılan tüm işlemler tekrarlandı. Şekilde görüldüğü gibi 2 adım sonunda w5
değerleri, girdi vektöründeki değerlere yaklaştı. Aynı işlemlerin tekrarlanması bizi
istenilen değerlere ulaştıracaktır.
22. Şekilde, özellik haritasında kazanan sinir hücresine ait
aktivasyon alanının algoritma ilerledikçe küçülmesi
gösterilmiştir.
Aktivasyon Alanı
23. SOM Ağlarının Kullanım Alanları
SOM ağları, hem verilerin kümelenmesinde hem de
görselleştirilmesi açısından tercih edilmektedir. Bu ağlar çok
boyutlu bir veriyi iki boyutlu bir haritaya indirgemektedir. Her bir
küme için oluşturulan referans vektörleri bir araya geldiğinde bir
haritayı meydana getirmektedir.
24. SOM Modelinde Kümelemeyi
Etkileyen Faktörler
1. Çıkış katmanındaki nöron sayısı
2. Verilerin normalleştirilmesi
3. Referans vektörlerine ilk değer atanması
4. Uzaklık ölçüsü
5. Öğrenme katsayısı ve Komşuluk değişkeni
