The document provides an introduction to neural networks. It discusses that neural networks are simplified models of the brain that can be used for classification, prediction, and noise reduction by recognizing patterns in inputs. Neural networks work by having many "neurons" that cooperate to perform the desired function through weighted connections between layers of neurons. The weights are determined through a training process where the network is presented with sample data and the weights are modified to better approximate the desired outputs.
Artificial neural networks (ANNs) are inspired by biological neural networks and are composed of interconnected processing elements called neurons. ANNs are configured through a learning process to solve problems like pattern recognition or data classification. Early research in the 1940s and 1950s laid the foundations, like McCulloch and Pitts developing the first neural network model and Hebb developing the first learning rule. ANNs use weighted connections and activation functions to learn from examples through training. Feedforward and feedback networks differ in whether signals travel in one or both directions between layers of neurons. Perceptrons were influential early neural network models that could perform tasks linear programs could not.
The document discusses neural networks and their biological inspiration. It defines an artificial neural network as an information processing system modeled after the human brain. Neural networks can extract patterns from complex data, operate in parallel, and learn from experience. The document then covers biological neurons, characteristics of neural networks, popular neural network models, learning rules, and different types of learning.
- Artificial neural networks are inspired by biological neural networks and try to mimic their learning mechanisms by modifying synaptic strengths through an optimization process.
- Learning in neural networks can be formulated as a function approximation task where the network learns to approximate a function by minimizing an error measure through optimization of synaptic weights.
- A single hidden layer neural network is capable of learning nonlinear function approximations if general optimization methods are applied to update the synaptic weights.
Neural networks are parallel information processing systems inspired by the brain. They can learn from historical data to make classifications and predictions. A neural network consists of interconnected nodes called neurons that receive inputs, perform operations, and output results. By training neural networks on large amounts of labeled data using techniques like backpropagation, they can learn complex patterns to solve problems like image and speech recognition.
Artificial neural network for concrete mix designMonjurul Shuvo
This document describes a study using artificial neural networks (ANN) to predict concrete mix designs and compressive strengths. The study aims to construct ANN models to predict mix proportions for a target strength and to predict strength for given mix proportions. Data on 79 concrete mixes are used to train and test the ANN models. The results show the ANN can predict mix ratios with 99% accuracy and strengths with 98% accuracy, demonstrating ANN is an effective tool for concrete mix design that outperforms traditional methods. Parametric studies examine the effects of water-cement ratio, fines content, and mix ratios on strength. The document concludes ANN is a powerful tool that can be used in concrete mix design practice.
This document discusses neural networks and their applications in mobile game programming. It begins with definitions of standard deviation, root mean square, neurons, dendrites, and axons. It then explains the three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. The document also covers standard neural network uses like pattern recognition and control. It provides an in-depth explanation of perceptrons and how they work, including examples of pattern recognition and supervised learning algorithms. Finally, it discusses limitations of single-layer perceptrons and introduces multi-layer perceptrons and backpropagation training.
Dr. kiani artificial neural network lecture 1Parinaz Faraji
The document provides a history of neural networks, beginning with McCulloch and Pitts creating the first neural network model in 1943. It then discusses several important developments in neural networks including perceptrons in the 1950s and 1960s, backpropagation in the 1980s, and neural networks being implemented in semiconductors in the late 1980s. The document also includes diagrams and explanations of biological neurons, artificial neurons, different types of activation functions, and key aspects of neural network architectures.
Artificial neural networks are computational models inspired by biological neural networks. They are composed of artificial neurons that are connected and communicate with each other. Each neuron receives inputs, performs simple computations, and transmits outputs. The connections between neurons are associated with adaptive weights that are adjusted during learning. Neural networks can be trained to perform complex tasks like pattern recognition, prediction, and classification. They have many applications in business including data mining, resource allocation, and prediction.
Artificial neural networks (ANNs) are inspired by biological neural networks and are composed of interconnected processing elements called neurons. ANNs are configured through a learning process to solve problems like pattern recognition or data classification. Early research in the 1940s and 1950s laid the foundations, like McCulloch and Pitts developing the first neural network model and Hebb developing the first learning rule. ANNs use weighted connections and activation functions to learn from examples through training. Feedforward and feedback networks differ in whether signals travel in one or both directions between layers of neurons. Perceptrons were influential early neural network models that could perform tasks linear programs could not.
The document discusses neural networks and their biological inspiration. It defines an artificial neural network as an information processing system modeled after the human brain. Neural networks can extract patterns from complex data, operate in parallel, and learn from experience. The document then covers biological neurons, characteristics of neural networks, popular neural network models, learning rules, and different types of learning.
- Artificial neural networks are inspired by biological neural networks and try to mimic their learning mechanisms by modifying synaptic strengths through an optimization process.
- Learning in neural networks can be formulated as a function approximation task where the network learns to approximate a function by minimizing an error measure through optimization of synaptic weights.
- A single hidden layer neural network is capable of learning nonlinear function approximations if general optimization methods are applied to update the synaptic weights.
Neural networks are parallel information processing systems inspired by the brain. They can learn from historical data to make classifications and predictions. A neural network consists of interconnected nodes called neurons that receive inputs, perform operations, and output results. By training neural networks on large amounts of labeled data using techniques like backpropagation, they can learn complex patterns to solve problems like image and speech recognition.
Artificial neural network for concrete mix designMonjurul Shuvo
This document describes a study using artificial neural networks (ANN) to predict concrete mix designs and compressive strengths. The study aims to construct ANN models to predict mix proportions for a target strength and to predict strength for given mix proportions. Data on 79 concrete mixes are used to train and test the ANN models. The results show the ANN can predict mix ratios with 99% accuracy and strengths with 98% accuracy, demonstrating ANN is an effective tool for concrete mix design that outperforms traditional methods. Parametric studies examine the effects of water-cement ratio, fines content, and mix ratios on strength. The document concludes ANN is a powerful tool that can be used in concrete mix design practice.
This document discusses neural networks and their applications in mobile game programming. It begins with definitions of standard deviation, root mean square, neurons, dendrites, and axons. It then explains the three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. The document also covers standard neural network uses like pattern recognition and control. It provides an in-depth explanation of perceptrons and how they work, including examples of pattern recognition and supervised learning algorithms. Finally, it discusses limitations of single-layer perceptrons and introduces multi-layer perceptrons and backpropagation training.
Dr. kiani artificial neural network lecture 1Parinaz Faraji
The document provides a history of neural networks, beginning with McCulloch and Pitts creating the first neural network model in 1943. It then discusses several important developments in neural networks including perceptrons in the 1950s and 1960s, backpropagation in the 1980s, and neural networks being implemented in semiconductors in the late 1980s. The document also includes diagrams and explanations of biological neurons, artificial neurons, different types of activation functions, and key aspects of neural network architectures.
Artificial neural networks are computational models inspired by biological neural networks. They are composed of artificial neurons that are connected and communicate with each other. Each neuron receives inputs, performs simple computations, and transmits outputs. The connections between neurons are associated with adaptive weights that are adjusted during learning. Neural networks can be trained to perform complex tasks like pattern recognition, prediction, and classification. They have many applications in business including data mining, resource allocation, and prediction.
This document provides an overview of artificial neural networks including their history, applications, properties, and basic concepts like perceptrons, gradient descent, backpropagation, and multi-layer networks. It then gives an example of using a neural network for face recognition, describing the input/output encoding, network structure, training parameters, and achieving 90% accuracy on the test set. The document encourages the reader to try implementing and running the face recognition network code provided online.
1. Neural networks are inspired by biological neural networks in the brain and are made up of simple processing units called neurons.
2. Artificial neural networks use a layer of input neurons that receive information and pass it through connections to other neurons.
3. A neural network learns through a process of trial and error adjustment of the weights between neurons to minimize errors between the network's output and the desired output.
In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI.
Neural networks are modeled after the human brain and consist of interconnected nodes that process information using activation functions. They can be trained to recognize patterns in data and make predictions. The network is initialized with random weights and biases then trained via backpropagation to minimize an error function by adjusting the weights. Issues that can arise include overfitting, choosing the number of hidden layers and units, and multiple local minima. Bayesian neural networks place prior distributions over the weights to better model uncertainty. Ensemble methods like bagging and boosting can improve performance.
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
This document discusses neural networks and fuzzy logic. It explains that neural networks can learn from data and feedback but are viewed as "black boxes", while fuzzy logic models are easier to comprehend but do not come with a learning algorithm. It then describes how neuro-fuzzy systems combine these two approaches by using neural networks to construct fuzzy rule-based models or fuzzy partitions of the input space. Specifically, it outlines the Adaptive Network-based Fuzzy Inference System (ANFIS) architecture, which is functionally equivalent to fuzzy inference systems and can represent both Sugeno and Tsukamoto fuzzy models using a five-layer feedforward neural network structure.
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.
The document discusses artificial neural networks and their applications. It covers topics like biological inspiration for ANNs, why they are used, learning strategies and techniques, network architectures like MLP, activation functions, and applications in areas like pattern classification, time series forecasting, control, and optimization. Key applications mentioned include handwritten digit recognition, remote sensing, machine control, and predicting things like hospital stay length and gas prices. References on the topic are also provided.
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.
This document provides an overview of neural networks. It discusses that neural networks are composed of interconnected processing units similar to neurons in the brain. Neural networks can learn patterns from examples through training and are well-suited for problems that are difficult to solve with traditional algorithms. The document outlines common neural network architectures like feedforward and feedback networks. It also discusses neural network learning methods and applications.
1. Feed-forward neural networks are composed of nodes connected in a directed graph without feedback loops. Information flows from input to output nodes through one or more hidden layers.
2. Each node receives weighted input signals, calculates a weighted sum, and applies an activation function to determine its output. During training, weights are adjusted to minimize error between network outputs and desired targets.
3. Self-organizing maps are neural networks that use unsupervised learning to produce a low-dimensional representation of input patterns. They cluster multidimensional data onto a two-dimensional map based on topological similarity.
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.
Neural networks are composed of simple processing units (neurons) that are interconnected and can learn from data. Natural neural networks in the brain contain billions of neurons that communicate via electrochemical signals. Early artificial neural networks modeled neurons as simple processing units that sum their weighted inputs and use an activation function to determine their output. These networks had limitations in what functions they could represent. The development of multi-layer perceptrons overcame these limitations by introducing hidden layers that increased their computational and representational power.
This document describes research applying artificial neural networks to magnetotelluric data to determine subsurface layer structures. Key points:
- Researchers developed a three-layer neural network model trained with backpropagation to locate subsurface layers from magnetotelluric data. Resilient propagation training was found to be most effective.
- The network was trained on synthetic 1D magnetotelluric data for different layer resistivities and thicknesses, and tested on synthetic and real field data.
- Results showed the neural network approach produced fast, accurate, and objective estimates of subsurface resistivity and depth that correlated well with conventional serial algorithms. This validated neural networks as a useful tool for magnetotelluric inversion and
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
This document discusses neural networks and their biological and technical underpinnings. It covers how natural neural networks operate using electrochemical signals and thresholds. It also discusses early artificial neural network models like McCulloch-Pitts networks and perceptrons. Perceptrons are defined as single-layer feedforward networks and can only represent linearly separable functions. The document introduces the concept of adding hidden layers to networks to increase their computational power and ability to represent more complex functions like XOR.
Artificial neural networks and its applications PoojaKoshti2
This presentation provides an overview of artificial neural networks (ANN), including what they are, how they work, different types, and applications. It defines ANN as biologically inspired simulations used for tasks like clustering, classification, and pattern recognition. The presentation explains that ANN learn by processing information in parallel through nodes and weighted connections, similar to the human brain. It also outlines various ANN architectures, such as perceptrons, recurrent networks, and convolutional networks. Finally, the presentation discusses common applications of ANN in domains like process control, medical diagnosis, and targeted marketing.
Artificial neural networks (ANNs) are a machine learning approach modeled after the human brain. ANNs consist of artificial neurons that are connected in a network similar to biological neurons. Each neuron receives inputs, applies an activation function, and outputs a value. ANNs are specified by their neuron model, architecture including connections between neurons with weights, and a learning algorithm to train the network by modifying weights. ANNs have advantages like storing information on the entire network, working with incomplete knowledge, fault tolerance, and parallel processing. However, they also have disadvantages such as hardware dependence, unexplained behavior, difficulty determining network structure, and unknown optimal training duration.
Neural networks are computing systems inspired by biological neural networks in the brain. They are composed of interconnected artificial neurons that process information using a connectionist approach. Neural networks can be used for applications like pattern recognition, classification, prediction, and filtering. They have the ability to learn from and recognize patterns in data, allowing them to perform complex tasks. Some examples of neural network applications discussed include face recognition, handwritten digit recognition, fingerprint recognition, medical diagnosis, and more.
Neural networks are mathematical models inspired by biological neural networks. They are useful for pattern recognition and data classification through a learning process of adjusting synaptic connections between neurons. A neural network maps input nodes to output nodes through an arbitrary number of hidden nodes. It is trained by presenting examples to adjust weights using methods like backpropagation to minimize error between actual and predicted outputs. Neural networks have advantages like noise tolerance and not requiring assumptions about data distributions. They have applications in finance, marketing, and other fields, though designing optimal network topology can be challenging.
This document provides an overview of artificial neural networks including their history, applications, properties, and basic concepts like perceptrons, gradient descent, backpropagation, and multi-layer networks. It then gives an example of using a neural network for face recognition, describing the input/output encoding, network structure, training parameters, and achieving 90% accuracy on the test set. The document encourages the reader to try implementing and running the face recognition network code provided online.
1. Neural networks are inspired by biological neural networks in the brain and are made up of simple processing units called neurons.
2. Artificial neural networks use a layer of input neurons that receive information and pass it through connections to other neurons.
3. A neural network learns through a process of trial and error adjustment of the weights between neurons to minimize errors between the network's output and the desired output.
In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI.
Neural networks are modeled after the human brain and consist of interconnected nodes that process information using activation functions. They can be trained to recognize patterns in data and make predictions. The network is initialized with random weights and biases then trained via backpropagation to minimize an error function by adjusting the weights. Issues that can arise include overfitting, choosing the number of hidden layers and units, and multiple local minima. Bayesian neural networks place prior distributions over the weights to better model uncertainty. Ensemble methods like bagging and boosting can improve performance.
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
This document discusses neural networks and fuzzy logic. It explains that neural networks can learn from data and feedback but are viewed as "black boxes", while fuzzy logic models are easier to comprehend but do not come with a learning algorithm. It then describes how neuro-fuzzy systems combine these two approaches by using neural networks to construct fuzzy rule-based models or fuzzy partitions of the input space. Specifically, it outlines the Adaptive Network-based Fuzzy Inference System (ANFIS) architecture, which is functionally equivalent to fuzzy inference systems and can represent both Sugeno and Tsukamoto fuzzy models using a five-layer feedforward neural network structure.
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.
The document discusses artificial neural networks and their applications. It covers topics like biological inspiration for ANNs, why they are used, learning strategies and techniques, network architectures like MLP, activation functions, and applications in areas like pattern classification, time series forecasting, control, and optimization. Key applications mentioned include handwritten digit recognition, remote sensing, machine control, and predicting things like hospital stay length and gas prices. References on the topic are also provided.
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.
This document provides an overview of neural networks. It discusses that neural networks are composed of interconnected processing units similar to neurons in the brain. Neural networks can learn patterns from examples through training and are well-suited for problems that are difficult to solve with traditional algorithms. The document outlines common neural network architectures like feedforward and feedback networks. It also discusses neural network learning methods and applications.
1. Feed-forward neural networks are composed of nodes connected in a directed graph without feedback loops. Information flows from input to output nodes through one or more hidden layers.
2. Each node receives weighted input signals, calculates a weighted sum, and applies an activation function to determine its output. During training, weights are adjusted to minimize error between network outputs and desired targets.
3. Self-organizing maps are neural networks that use unsupervised learning to produce a low-dimensional representation of input patterns. They cluster multidimensional data onto a two-dimensional map based on topological similarity.
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.
Neural networks are composed of simple processing units (neurons) that are interconnected and can learn from data. Natural neural networks in the brain contain billions of neurons that communicate via electrochemical signals. Early artificial neural networks modeled neurons as simple processing units that sum their weighted inputs and use an activation function to determine their output. These networks had limitations in what functions they could represent. The development of multi-layer perceptrons overcame these limitations by introducing hidden layers that increased their computational and representational power.
This document describes research applying artificial neural networks to magnetotelluric data to determine subsurface layer structures. Key points:
- Researchers developed a three-layer neural network model trained with backpropagation to locate subsurface layers from magnetotelluric data. Resilient propagation training was found to be most effective.
- The network was trained on synthetic 1D magnetotelluric data for different layer resistivities and thicknesses, and tested on synthetic and real field data.
- Results showed the neural network approach produced fast, accurate, and objective estimates of subsurface resistivity and depth that correlated well with conventional serial algorithms. This validated neural networks as a useful tool for magnetotelluric inversion and
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
This document discusses neural networks and their biological and technical underpinnings. It covers how natural neural networks operate using electrochemical signals and thresholds. It also discusses early artificial neural network models like McCulloch-Pitts networks and perceptrons. Perceptrons are defined as single-layer feedforward networks and can only represent linearly separable functions. The document introduces the concept of adding hidden layers to networks to increase their computational power and ability to represent more complex functions like XOR.
Artificial neural networks and its applications PoojaKoshti2
This presentation provides an overview of artificial neural networks (ANN), including what they are, how they work, different types, and applications. It defines ANN as biologically inspired simulations used for tasks like clustering, classification, and pattern recognition. The presentation explains that ANN learn by processing information in parallel through nodes and weighted connections, similar to the human brain. It also outlines various ANN architectures, such as perceptrons, recurrent networks, and convolutional networks. Finally, the presentation discusses common applications of ANN in domains like process control, medical diagnosis, and targeted marketing.
Artificial neural networks (ANNs) are a machine learning approach modeled after the human brain. ANNs consist of artificial neurons that are connected in a network similar to biological neurons. Each neuron receives inputs, applies an activation function, and outputs a value. ANNs are specified by their neuron model, architecture including connections between neurons with weights, and a learning algorithm to train the network by modifying weights. ANNs have advantages like storing information on the entire network, working with incomplete knowledge, fault tolerance, and parallel processing. However, they also have disadvantages such as hardware dependence, unexplained behavior, difficulty determining network structure, and unknown optimal training duration.
Neural networks are computing systems inspired by biological neural networks in the brain. They are composed of interconnected artificial neurons that process information using a connectionist approach. Neural networks can be used for applications like pattern recognition, classification, prediction, and filtering. They have the ability to learn from and recognize patterns in data, allowing them to perform complex tasks. Some examples of neural network applications discussed include face recognition, handwritten digit recognition, fingerprint recognition, medical diagnosis, and more.
Neural networks are mathematical models inspired by biological neural networks. They are useful for pattern recognition and data classification through a learning process of adjusting synaptic connections between neurons. A neural network maps input nodes to output nodes through an arbitrary number of hidden nodes. It is trained by presenting examples to adjust weights using methods like backpropagation to minimize error between actual and predicted outputs. Neural networks have advantages like noise tolerance and not requiring assumptions about data distributions. They have applications in finance, marketing, and other fields, though designing optimal network topology can be challenging.
This document provides an overview of artificial neural networks. It discusses the biological neuron model that inspired artificial neural networks. The key components of an artificial neuron are inputs, weights, summation, and an activation function. Neural networks have an interconnected architecture with layers of nodes. Learning involves modifying the weights through algorithms like backpropagation to minimize error. Neural networks can perform supervised or unsupervised learning. Their advantages include handling complex nonlinear problems, learning from data, and adapting to new situations.
Slides for my talk discussing research on the Evolutionary Neural Network Encoder of Shenanigans (ENNEoS), a proof-of-concept encoder for shellcode. The software uses genetic algorithms to evolve neural networks that contain the shellcode and output it on demand so that it can be executed in memory.
This lecture is about NEURAL NETWORKS WITH “R”. Artificial Neural Networks (ANNs) that starting from the mechanisms regulating natural neural networks, plan to simulate human thinking. The discipline of ANN arose from the thought of mimicking the functioning of the same human brain that was trying to solve the problem. The Machine learning is a branch of AI which helps computers to program themselves based on the input data.
In this regard, Machine learning gives AI the ability to do data-based problem solving. This lecture shows applications.
Neuromorphic computing is a new computing paradigm inspired by the workings of the human brain.
It involves the use of artificial neural networks that mimic the structure and function of biological neurons.
These networks are implemented in specialized hardware that is designed to optimize the performance of neural computations.
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This document discusses using neural networks to hide shellcode. It introduces ENNEoS, a proof-of-concept tool that uses genetic algorithms and neural networks to encode shellcode in a way that is difficult for antivirus to detect. ENNEoS evolves the structure and weights of recurrent neural networks to store and output shellcode based on a fitness function that scores how close the output is to the desired shellcode characters. A demo is shown of the encoder generating neural networks that a loader program then uses to retrieve and execute the hidden shellcode. Future work is discussed to improve the practicality and performance of the technique.
Decision trees are a supervised learning technique that can be used for both classification and regression problems. It is a tree-structured classifier with internal nodes representing features, branches representing decision rules, and leaf nodes representing outcomes. Decision trees have decision nodes that make decisions and have multiple branches, and leaf nodes that are the output of decisions without further branches. Decisions are made based on features of the given dataset.
Decision trees are a supervised learning technique that can be used for both classification and regression problems. It has a tree structure with internal nodes representing features, branches representing decision rules, and leaf nodes representing the outcome. Decision nodes make decisions and have multiple branches, while leaf nodes are the outputs of decisions and have no further branches. The decisions are based on features of the given dataset.
Decision trees are a supervised learning technique that can be used for both classification and regression problems. It has a tree structure with internal nodes representing features, branches representing decision rules, and leaf nodes representing the outcome. Decision nodes make decisions and have multiple branches, while leaf nodes are the outputs of decisions and have no further branches. The decisions are based on features of the given dataset.
- 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.
deep learning from scratch chapter 3 neural networkJaey Jeong
This document discusses neural networks and their implementation. It covers neural network architecture including input, output, and hidden layers. Activation functions like sigmoid, ReLU, and their purpose in making networks nonlinear are explained. Implementation details like using NumPy multidimensional arrays, normalization, and batching are provided. The output layer design for classification vs regression is discussed. Common activation functions for the output layer like identity and softmax are also covered.
This document provides an introduction to neural networks, including their basic components and types. It discusses neurons, activation functions, different types of neural networks based on connection type, topology, and learning methods. It also covers applications of neural networks in areas like pattern recognition and control systems. Neural networks have advantages like the ability to learn from experience and handle incomplete information, but also disadvantages like the need for training and high processing times for large networks. In conclusion, neural networks can provide more human-like artificial intelligence by taking approximation and hard-coded reactions out of AI design, though they still require fine-tuning.
This document provides an overview of deep learning concepts including neural networks, supervised and unsupervised learning, and key terms. It explains that deep learning uses neural networks with many hidden layers to learn features directly from raw data. Supervised learning algorithms learn from labeled examples to perform classification or regression on unseen data. Unsupervised learning finds patterns in unlabeled data. Key terms defined include neurons, activation functions, loss functions, optimizers, epochs, batches, and hyperparameters.
This document provides an overview of neural networks. It discusses how neural networks are inspired by biological neurons and are composed of interconnected processing units called neurons arranged in layers. There are different types of neural networks defined by their connection patterns, topology, and learning methods. Neural networks are applied to problems like pattern recognition, forecasting, and control systems. They have advantages like the ability to learn from data and handle incomplete information, but also disadvantages like requiring extensive training.
A neural network is a network or circuit of neurons.
The neural network has layers of units where each layer takes some value from the previous layer.
That way, systems that are based on neural network can
compute inputs to get the needed output.
The same way neurons pass signals around the brain, and values
are passed from one unit in an artificial neural network to another
to perform the required computation and get new value as output.
The united are layers, forming a system that starts from the layers used for imputing to layer that is used to provide the output
The document provides an introduction to artificial neural networks. It discusses biological neurons and how artificial neurons are modeled. The key aspects covered are:
- Artificial neural networks (ANNs) are modeled after biological neural systems and are comprised of basic units (nodes/neurons) connected by links with weights.
- ANNs learn by adjusting the weights of connections between nodes through training algorithms like backpropagation. This allows the network to continually learn from examples.
- The network is organized into layers with connections only between adjacent layers in a feedforward network. Backpropagation is used to calculate weight adjustments to minimize error between actual and expected outputs.
- Learning can be supervised, using examples of inputs and outputs, or
Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
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"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor IvaniukFwdays
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Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
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Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
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The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
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inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
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Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
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Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
Northern Engraving | Nameplate Manufacturing Process - 2024
Neural networks.cheungcannonnotes
1. An Introduction to Neural Networks
Vincent Cheung
Kevin Cannons
Signal & Data Compression Laboratory
Electrical & Computer Engineering
University of Manitoba
Winnipeg, Manitoba, Canada
Advisor: Dr. W. Kinsner
May 27, 2002
2. Neural Networks
Outline
● Fundamentals
● Classes
● Design and Verification
● Results and Discussion
● Conclusion
Cheung/Cannons 1
3. Fundamentals Neural Networks
What Are Artificial Neural Networks?
● An extremely simplified model of the brain
Classes
● Essentially a function approximator
► Transforms inputs into outputs to the best of its ability
Design
Inputs Outputs
Results
Inputs NN Outputs
Cheung/Cannons 2
4. Fundamentals Neural Networks
What Are Artificial Neural Networks?
● Composed of many “neurons” that co-operate
Classes
to perform the desired function
Design
Results
Cheung/Cannons 3
5. Fundamentals Neural Networks
What Are They Used For?
● Classification
Classes
► Pattern recognition, feature extraction, image
matching
● Noise Reduction
► Recognize patterns in the inputs and produce
Design
noiseless outputs
● Prediction
► Extrapolation based on historical data
Results
Cheung/Cannons 4
6. Fundamentals Neural Networks
Why Use Neural Networks?
● Ability to learn
Classes
► NN’s figure out how to perform their function on their own
► Determine their function based only upon sample inputs
● Ability to generalize
i.e. produce reasonable outputs for inputs it has not been
Design
►
taught how to deal with
Results
Cheung/Cannons 5
7. Fundamentals Neural Networks
How Do Neural Networks Work?
● The output of a neuron is a function of the
Classes
weighted sum of the inputs plus a bias
i1 w1
w2
i2
w3 Neuron Output = f(i1w1 + i2w2 + i3w3 + bias)
Design
i3
bias
Results
● The function of the entire neural network is simply
the computation of the outputs of all the neurons
► An entirely deterministic calculation
Cheung/Cannons 6
8. Fundamentals Neural Networks
Activation Functions
● Applied to the weighted sum of the inputs of a
Classes
neuron to produce the output
● Majority of NN’s use sigmoid functions
► Smooth, continuous, and monotonically increasing
(derivative is always positive)
Design
► Bounded range - but never reaches max or min
■ Consider “ON” to be slightly less than the max and “OFF” to
be slightly greater than the min
Results
Cheung/Cannons 7
9. Fundamentals Neural Networks
Activation Functions
● The most common sigmoid function used is the
Classes
logistic function
► f(x) = 1/(1 + e-x)
► The calculation of derivatives are important for neural
networks and the logistic function has a very nice
Design
derivative
■ f’(x) = f(x)(1 - f(x))
● Other sigmoid functions also used
hyperbolic tangent
Results
►
► arctangent
● The exact nature of the function has little effect on
the abilities of the neural network
Cheung/Cannons 8
10. Fundamentals Neural Networks
Where Do The Weights Come From?
● The weights in a neural network are the most
Classes
important factor in determining its function
● Training is the act of presenting the network with
some sample data and modifying the weights to
better approximate the desired function
Design
● There are two main types of training
► Supervised Training
■ Supplies the neural network with inputs and the desired
Results
outputs
■ Response of the network to the inputs is measured
The weights are modified to reduce the difference between
the actual and desired outputs
Cheung/Cannons 9
11. Fundamentals Neural Networks
Where Do The Weights Come From?
► Unsupervised Training
Classes
■ Only supplies inputs
■ The neural network adjusts its own weights so that similar
inputs cause similar outputs
The network identifies the patterns and differences in the
inputs without any external assistance
Design
● Epoch
■ One iteration through the process of providing the network
with an input and updating the network's weights
■ Typically many epochs are required to train the neural
Results
network
Cheung/Cannons 10
12. Fundamentals Neural Networks
Perceptrons
● First neural network with the ability to learn
Classes
● Made up of only input neurons and output neurons
● Input neurons typically have two states: ON and OFF
● Output neurons use a simple threshold activation function
Design
● In basic form, can only solve linear problems
► Limited applications
.5
Results
.2
.8
Input Neurons Weights Output Neuron
Cheung/Cannons 11
13. Fundamentals Neural Networks
How Do Perceptrons Learn?
● Uses supervised training
Classes
● If the output is not correct, the weights are
adjusted according to the formula:
■ wnew = wold + α(desired – output)*input α is the learning rate
Design
1 0.5
0.2
0 1
1 0.8
Results
Assume Output was supposed to be 0
update the weights
1 * 0.5 + 0 * 0.2 + 1 * 0.8 = 1.3
Assuming Output Threshold = 1.2 Assume α = 1
1.3 > 1.2 W 1new = 0.5 + 1*(0-1)*1 = -0.5
W 2new = 0.2 + 1*(0-1)*0 = 0.2
W 3new = 0.8 + 1*(0-1)*1 = -0.2
Cheung/Cannons 12
14. Fundamentals Neural Networks
Multilayer Feedforward Networks
● Most common neural network
Classes
● An extension of the perceptron
► Multiple layers
■ The addition of one or more “hidden” layers in between the
input and output layers
Design
► Activation function is not simply a threshold
■ Usually a sigmoid function
► A general function approximator
■ Not limited to linear problems
Results
● Information flows in one direction
► The outputs of one layer act as inputs to the next layer
Cheung/Cannons 13
16. Fundamentals Neural Networks
Backpropagation
● Most common method of obtaining the many
Classes
weights in the network
● A form of supervised training
● The basic backpropagation algorithm is based on
Design
minimizing the error of the network using the
derivatives of the error function
► Simple
► Slow
Results
► Prone to local minima issues
Cheung/Cannons 15
17. Fundamentals Neural Networks
Backpropagation
● Most common measure of error is the mean
Classes
square error:
E = (target – output)2
● Partial derivatives of the error wrt the weights:
Design
► Output Neurons:
let: δj = f’(netj) (targetj – outputj) j = output neuron
∂E/∂wji = -outputi δj i = neuron in last hidden
Results
► Hidden Neurons:
j = hidden neuron
let: δj = f’(netj) Σ(δkwkj)
i = neuron in previous layer
∂E/∂wji = -outputi δj k = neuron in next layer
Cheung/Cannons 16
18. Fundamentals Neural Networks
Backpropagation
● Calculation of the derivatives flows backwards
Classes
through the network, hence the name,
backpropagation
● These derivatives point in the direction of the
maximum increase of the error function
Design
● A small step (learning rate) in the opposite
direction will result in the maximum decrease of
the (local) error function:
Results
wnew = wold – α ∂E/∂wold
where α is the learning rate
Cheung/Cannons 17
19. Fundamentals Neural Networks
Backpropagation
● The learning rate is important
Classes
► Too small
■ Convergence extremely slow
► Too large
■ May not converge
Design
● Momentum
► Tends to aid convergence
► Applies smoothed averaging to the change in weights:
Results
∆new = β∆old - α ∂E/∂wold β is the momentum coefficient
wnew = wold + ∆new
► Acts as a low-pass filter by reducing rapid fluctuations
Cheung/Cannons 18
20. Fundamentals Neural Networks
Local Minima
● Training is essentially minimizing the mean square
Classes
error function
► Key problem is avoiding local minima
► Traditional techniques for avoiding local minima:
■ Simulated annealing
Design
Perturb the weights in progressively smaller amounts
■ Genetic algorithms
Use the weights as chromosomes
Apply natural selection, mating, and mutations to these
chromosomes
Results
Cheung/Cannons 19
21. Fundamentals Neural Networks
Counterpropagation (CP) Networks
● Another multilayer feedforward network
Classes
● Up to 100 times faster than backpropagation
● Not as general as backpropagation
● Made up of three layers:
Design
► Input
► Kohonen
► Grossberg (Output)
Results
Inputs Input Kohonen Grossberg Outputs
Layer Layer Layer
Cheung/Cannons 20
22. Fundamentals Neural Networks
How Do They Work?
● Kohonen Layer:
Classes
► Neurons in the Kohonen layer sum all of the weighted
inputs received
► The neuron with the largest sum outputs a 1 and the
other neurons output 0
Design
● Grossberg Layer:
► Each Grossberg neuron merely outputs the weight of the
connection between itself and the one active Kohonen
neuron
Results
Cheung/Cannons 21
23. Fundamentals Neural Networks
Why Two Different Types of Layers?
● More accurate representation of biological neural
Classes
networks
● Each layer has its own distinct purpose:
► Kohonen layer separates inputs into separate classes
■ Inputs in the same class will turn on the same Kohonen
Design
neuron
► Grossberg layer adjusts weights to obtain acceptable
outputs for each class
Results
Cheung/Cannons 22
24. Fundamentals Neural Networks
Training a CP Network
● Training the Kohonen layer
Classes
► Uses unsupervised training
► Input vectors are often normalized
► The one active Kohonen neuron updates its weights
according to the formula:
Design
wnew = wold + α(input - wold)
where α is the learning rate
Results
■ The weights of the connections are being modified to more
closely match the values of the inputs
■ At the end of training, the weights will approximate the
average value of the inputs in that class
Cheung/Cannons 23
25. Fundamentals Neural Networks
Training a CP Network
● Training the Grossberg layer
Classes
► Uses supervised training
► Weight update algorithm is similar to that used in
backpropagation
Design
Results
Cheung/Cannons 24
26. Fundamentals Neural Networks
Hidden Layers and Neurons
● For most problems, one layer is sufficient
Classes
● Two layers are required when the function is
discontinuous
● The number of neurons is very important:
Design
► Too few
■ Underfit the data – NN can’t learn the details
► Too many
■ Overfit the data – NN learns the insignificant details
Results
► Start small and increase the number until satisfactory
results are obtained
Cheung/Cannons 25
27. Fundamentals Neural Networks
Overfitting
Classes
Design
Training
Test
W ell fit
Overfit
Results
Cheung/Cannons 26
28. Fundamentals Neural Networks
How is the Training Set Chosen?
● Overfitting can also occur if a “good” training set is
Classes
not chosen
● What constitutes a “good” training set?
► Samples must represent the general population
Samples must contain members of each class
Design
►
► Samples in each class must contain a wide range of
variations or noise effect
Results
Cheung/Cannons 27
29. Fundamentals Neural Networks
Size of the Training Set
● The size of the training set is related to the
Classes
number of hidden neurons
► Eg. 10 inputs, 5 hidden neurons, 2 outputs:
► 11(5) + 6(2) = 67 weights (variables)
► If only 10 training samples are used to determine these
Design
weights, the network will end up being overfit
■ Any solution found will be specific to the 10 training
samples
■ Analogous to having 10 equations, 67 unknowns you
Results
can come up with a specific solution, but you can’t find the
general solution with the given information
Cheung/Cannons 28
30. Fundamentals Neural Networks
Training and Verification
● The set of all known samples is broken into two
Classes
orthogonal (independent) sets:
► Training set
■ A group of samples used to train the neural network
► Testing set
Design
■ A group of samples used to test the performance of the
neural network
■ Used to estimate the error rate
Results
Known Samples
Training Testing
Set Set
Cheung/Cannons 29
31. Fundamentals Neural Networks
Verification
● Provides an unbiased test of the quality of the
Classes
network
● Common error is to “test” the neural network using
the same samples that were used to train the
neural network
Design
► The network was optimized on these samples, and will
obviously perform well on them
► Doesn’t give any indication as to how well the network
will be able to classify inputs that weren’t in the training
Results
set
Cheung/Cannons 30
32. Fundamentals Neural Networks
Verification
● Various metrics can be used to grade the
Classes
performance of the neural network based upon the
results of the testing set
► Mean square error, SNR, etc.
● Resampling is an alternative method of estimating
Design
error rate of the neural network
► Basic idea is to iterate the training and testing
procedures multiple times
Results
► Two main techniques are used:
■ Cross-Validation
■ Bootstrapping
Cheung/Cannons 31
33. Fundamentals Neural Networks
Results and Discussion
● A simple toy problem was used to test the
operation of a perceptron
Classes
● Provided the perceptron with 5 pieces of
information about a face – the individual’s hair,
eye, nose, mouth, and ear type
Design
► Each piece of information could take a value of +1 or -1
■ +1 indicates a “girl” feature
■ -1 indicates a “guy” feature
● The individual was to be classified as a girl if the
Results
face had more “girl” features than “guy” features
and a boy otherwise
Cheung/Cannons 32
34. Fundamentals Neural Networks
Results and Discussion
● Constructed a perceptron with 5 inputs and 1
Classes
output
Design
Face Input Output Output value
Feature neurons neuron indicating
Input boy or girl
Values
Results
● Trained the perceptron with 24 out of the 32
possible inputs over 1000 epochs
● The perceptron was able to classify the faces that
were not in the training set
Cheung/Cannons 33
35. Fundamentals Neural Networks
Results and Discussion
● A number of toy problems were tested on
multilayer feedforward NN’s with a single hidden
Classes
layer and backpropagation:
► Inverter
■ The NN was trained to simply output 0.1 when given a “1”
and 0.9 when given a “0”
Design
A demonstration of the NN’s ability to memorize
■ 1 input, 1 hidden neuron, 1 output
■ With learning rate of 0.5 and no momentum, it took about
3,500 epochs for sufficient training
■ Including a momentum coefficient of 0.9 reduced the
Results
number of epochs required to about 250
Cheung/Cannons 34
36. Fundamentals Neural Networks
Results and Discussion
► Inverter (continued)
Classes
■ Increasing the learning rate decreased the training time
without hampering convergence for this simple example
■ Increasing the epoch size, the number of samples per
epoch, decreased the number of epochs required and
seemed to aid in convergence (reduced fluctuations)
Design
■ Increasing the number of hidden neurons decreased the
number of epochs required
Allowed the NN to better memorize the training set – the goal
of this toy problem
Not recommended to use in “real” problems, since the NN
Results
loses its ability to generalize
Cheung/Cannons 35
37. Fundamentals Neural Networks
Results and Discussion
► AND gate
Classes
■ 2 inputs, 2 hidden neurons, 1 output
■ About 2,500 epochs were required when using momentum
► XOR gate
■ Same as AND gate
Design
► 3-to-8 decoder
■ 3 inputs, 3 hidden neurons, 8 outputs
■ About 5,000 epochs were required when using momentum
Results
Cheung/Cannons 36
38. Fundamentals Neural Networks
Results and Discussion
► Absolute sine function approximator (|sin(x)|)
Classes
■ A demonstration of the NN’s ability to learn the desired
function, |sin(x)|, and to generalize
■ 1 input, 5 hidden neurons, 1 output
■ The NN was trained with samples between –π/2 and π/2
The inputs were rounded to one decimal place
Design
The desired targets were scaled to between 0.1 and 0.9
■ The test data contained samples in between the training
samples (i.e. more than 1 decimal place)
The outputs were translated back to between 0 and 1
Results
■ About 50,000 epochs required with momentum
■ Not smooth function at 0 (only piece-wise continuous)
Cheung/Cannons 37
39. Fundamentals Neural Networks
Results and Discussion
2
► Gaussian function approximator (e-x )
Classes
■ 1 input, 2 hidden neurons, 1 output
■ Similar to the absolute sine function approximator, except
that the domain was changed to between -3 and 3
■ About 10,000 epochs were required with momentum
■ Smooth function
Design
Results
Cheung/Cannons 38
40. Fundamentals Neural Networks
Results and Discussion
► Primality tester
Classes
■ 7 inputs, 8 hidden neurons, 1 output
■ The input to the NN was a binary number
■ The NN was trained to output 0.9 if the number was prime
and 0.1 if the number was composite
Classification and memorization test
Design
■ The inputs were restricted to between 0 and 100
■ About 50,000 epochs required for the NN to memorize the
classifications for the training set
No attempts at generalization were made due to the
Results
complexity of the pattern of prime numbers
■ Some issues with local minima
Cheung/Cannons 39
41. Fundamentals Neural Networks
Results and Discussion
► Prime number generator
■ Provide the network with a seed, and a prime number of the
Classes
same order should be returned
■ 7 inputs, 4 hidden neurons, 7 outputs
■ Both the input and outputs were binary numbers
■ The network was trained as an autoassociative network
Design
Prime numbers from 0 to 100 were presented to the network
and it was requested that the network echo the prime
numbers
The intent was to have the network output the closest prime
number when given a composite number
■ After one million epochs, the network was successfully able
Results
to produce prime numbers for about 85 - 90% of the
numbers between 0 and 100
■ Using Gray code instead of binary did not improve results
■ Perhaps needs a second hidden layer, or implement some
heuristics to reduce local minima issues
Cheung/Cannons 40
42. Neural Networks
Conclusion
● The toy examples confirmed the basic operation of
neural networks and also demonstrated their
ability to learn the desired function and generalize
when needed
● The ability of neural networks to learn and
generalize in addition to their wide range of
applicability makes them very powerful tools
Cheung/Cannons 41
44. Neural Networks
Acknowledgements
● Natural Sciences and Engineering Research
Council (NSERC)
● University of Manitoba
Cheung/Cannons 43
45. Neural Networks
References
[AbDo99] H. Abdi, D. Valentin, B. Edelman, Neural Networks, Thousand Oaks, CA: SAGE Publication
Inc., 1999.
[Hayk94] S. Haykin, Neural Networks, New York, NY: Nacmillan College Publishing Company, Inc., 1994.
[Mast93] T. Masters, Practial Neural Network Recipes in C++, Toronto, ON: Academic Press, Inc., 1993.
[Scha97] R. Schalkoff, Artificial Neural Networks, Toronto, ON: the McGraw-Hill Companies, Inc., 1997.
[WeKu91] S. M. Weiss and C. A. Kulikowski, Computer Systems That Learn, San Mateo, CA: Morgan
Kaufmann Publishers, Inc., 1991.
[Wass89] P. D. Wasserman, Neural Computing: Theory and Practice, New York, NY: Van Nostrand
Reinhold, 1989.
Cheung/Cannons 44