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nil-100128213838-phpapp02.pptx
This document provides an overview of neural networks, including their biological inspiration, basic components like neurons and activation functions, different types of neural networks based on connection type, topology and learning method, and applications. It discusses how neural networks can learn from experience to improve performance and deal with incomplete information. The advantages are the ability to perform non-linear tasks and continue working if some elements fail, while disadvantages include the need for training and high processing times for large networks.
nil-100128213838-phpapp02.pdf
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.
Presentationnnnn
This document provides an overview of neural networks, including their basic components and types. It discusses neurons, activation functions, and different neural network architectures like feedforward and feedback networks. It also covers supervised, unsupervised, and reinforcement learning methods. Applications of neural networks are described along with their advantages in learning from experience and dealing with incomplete information, and disadvantages like long training times.
Introduction To Neural Network
Neural networks are a method of computing inspired by biological neural systems. They are comprised of interconnected nodes that can learn from data using a variety of supervised, unsupervised, and reinforcement learning techniques. Neural networks have many applications including pattern recognition, control systems, mobile computing, and health care. While they can learn without being explicitly programmed, they require extensive training and processing power for large networks.
neural network
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.
Ann
This document provides an introduction to neural networks, including their basic components of neurons, different types such as feed-forward and feedback networks, and applications such as pattern recognition and forecasting. It discusses the advantages of neural networks in their ability to learn without reprogramming and continue working even if some elements fail. Disadvantages include the need for training and high processing times for large networks.
Unit one ppt of deeep learning which includes Ann cnn
Deep learning involves using neural networks with multiple layers to automatically learn patterns from large amounts of data. The document discusses the working of deep learning networks, which take raw input data and pass it through successive hidden layers to determine higher-level features until reaching the output layer. It also covers applications of deep learning like image recognition and Amazon Alexa, as well as advantages such as automatic feature learning and ability to handle complex datasets.
Deep learning summary
This document provides an overview of deep learning and common deep learning concepts. It discusses that deep learning uses complex neural networks to determine representations of data, rather than requiring humans to engineer features. It also describes convolutional neural networks and how they are better than fully connected networks for tasks like image recognition. Additionally, it covers transfer learning and how pre-trained models can be adapted to new tasks by retraining final layers, reducing data and computation needs. Common deep learning architectures mentioned include AlexNet, VGG16, Inception and MobileNets.
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nil-100128213838-phpapp02.pptx
This document provides an overview of neural networks, including their biological inspiration, basic components like neurons and activation functions, different types of neural networks based on connection type, topology and learning method, and applications. It discusses how neural networks can learn from experience to improve performance and deal with incomplete information. The advantages are the ability to perform non-linear tasks and continue working if some elements fail, while disadvantages include the need for training and high processing times for large networks.
nil-100128213838-phpapp02.pdf
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.
Presentationnnnn
This document provides an overview of neural networks, including their basic components and types. It discusses neurons, activation functions, and different neural network architectures like feedforward and feedback networks. It also covers supervised, unsupervised, and reinforcement learning methods. Applications of neural networks are described along with their advantages in learning from experience and dealing with incomplete information, and disadvantages like long training times.
Introduction To Neural Network
Neural networks are a method of computing inspired by biological neural systems. They are comprised of interconnected nodes that can learn from data using a variety of supervised, unsupervised, and reinforcement learning techniques. Neural networks have many applications including pattern recognition, control systems, mobile computing, and health care. While they can learn without being explicitly programmed, they require extensive training and processing power for large networks.
neural network
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.
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This document provides an introduction to neural networks, including their basic components of neurons, different types such as feed-forward and feedback networks, and applications such as pattern recognition and forecasting. It discusses the advantages of neural networks in their ability to learn without reprogramming and continue working even if some elements fail. Disadvantages include the need for training and high processing times for large networks.
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Deep learning involves using neural networks with multiple layers to automatically learn patterns from large amounts of data. The document discusses the working of deep learning networks, which take raw input data and pass it through successive hidden layers to determine higher-level features until reaching the output layer. It also covers applications of deep learning like image recognition and Amazon Alexa, as well as advantages such as automatic feature learning and ability to handle complex datasets.
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This document provides an overview of deep learning and common deep learning concepts. It discusses that deep learning uses complex neural networks to determine representations of data, rather than requiring humans to engineer features. It also describes convolutional neural networks and how they are better than fully connected networks for tasks like image recognition. Additionally, it covers transfer learning and how pre-trained models can be adapted to new tasks by retraining final layers, reducing data and computation needs. Common deep learning architectures mentioned include AlexNet, VGG16, Inception and MobileNets.
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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.
Artificial Neural Networks for NIU session 2016 17
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.
Neural network
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each link has a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell : • DENDRITES: Accepts the inputs • SOMA : Process the inputs • AXON : Turns the processed inputs into outputs. • SYNAPSES : The electrochemical contact between the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network are represented by the x1 mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simply summed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): input signal. Weights (Dendrites): determines the importance of incoming value. Output (Axon): output to other neuron or of NN .
neuralnetwork.pptx
This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are:
- Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain.
- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have many applications including voice recognition, image recognition, robotics and more due to their ability to learn from large amounts of data.
neuralnetwork.pptx
This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are:
- Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain.
- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have various applications including voice recognition, image recognition, and robotics due to their ability to learn from large amounts of data.
Artificial Neural Network Learning Algorithm.ppt
This document discusses artificial neural networks (ANN). It describes ANN as computational models inspired by biological neural networks in the brain. ANN consist of interconnected nodes that process information, learn from data, and make predictions by adjusting connection weights. The document outlines different types of ANN architectures including single layer, feedforward, and recurrent networks. It also covers ANN learning techniques such as supervised, unsupervised, reinforcement, and competitive learning.
1 5
The document discusses training and testing neural networks. It covers topics like when a neural network is considered trained based on the application, controlling the training process with learning parameters, an iterative development process to improve the network, avoiding overtraining, and automating parts of the process.
Table of Contents
This project aims to implement a recurrent neural network module for the ACL Neural Toolkit to expand its capabilities. A team will design the neuron and network models, develop a training algorithm, and integrate it into the Toolkit over 5 months. Milestones include preliminary models and algorithms, and incorporating it into the Toolkit to offer a necessary addition that can solve more complex problems than other methods.
Neural Networks.pdf
This document provides an introduction to neural networks, including:
- An overview of neurons and how they are connected in a neural network.
- The main types of neural networks including feedforward, feedback, single layer, and multilayer networks.
- How neural networks learn through supervised, unsupervised, and reinforcement learning methods.
- Common applications of neural networks like pattern recognition, control systems, and forecasting.
- The advantages of neural networks in tasks linear programs cannot do and their ability to learn without reprogramming. Disadvantages include the need for training and high processing requirements for large networks.
Hard & soft computing
Hard computing relies on precise analytical models and computations to solve problems, while soft computing uses approximate techniques inspired by the human brain to provide usable solutions to complex problems. The document then discusses hard computing techniques like algorithms and mathematical formulas. It notes soft computing's advantages like robustness, low cost, and ability to solve complex problems. Soft computing techniques discussed include fuzzy logic, genetic algorithms, and neural networks. Applications mentioned include internet search, robotics, and speech recognition.
Artificial neural network
- An artificial neural network (ANN) is a computational model inspired by biological neural networks in the brain. ANNs contain interconnected nodes that can learn relationships and patterns from data using a process similar to biological learning.
- The basic ANN architecture consists of an input layer, hidden layers, and an output layer. Information flows from the input to output layers through the hidden layers as the network learns.
- There are different types of ANNs that vary in their structure and learning methods, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. ANNs can perform tasks using supervised, unsupervised, or reinforcement learning.
- ANNs have many applications including face recognition, ridesharing, handwriting
Artificial Neural Network
- An artificial neural network (ANN) is a computational model inspired by biological neural networks in the brain. ANNs contain interconnected nodes that can learn relationships and patterns from data through a process of training.
- The basic ANN architecture includes an input layer, hidden layers, and an output layer. Information flows from the input to the output layers through the hidden layers as the network learns.
- There are different types of ANNs that vary in their structure and learning methods, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. ANNs can perform tasks like face recognition, prediction, and classification through supervised, unsupervised, or reinforcement learning.
- While ANNs have advantages like fault tolerance
33.-Multi-Layer-Perceptron.pdf
This document provides an overview of multi-layer perceptrons (MLPs), also known as neural networks. It begins by discussing how perceptrons work, including taking inputs, multiplying them by weights, passing them through an activation function, and producing an output. MLPs consist of multiple stacked perceptron layers that allow them to solve more complex problems. Key aspects that enable deep learning with MLPs include backpropagation to optimize weights, tuning hyperparameters like the number of layers and activation functions, and using advanced training techniques involving learning rates, epochs, batches and optimizer algorithms.
Artificial Neural Network ANN
Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains.
An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.
Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
Artificial neural network model & hidden layers in multilayer artificial neur...
Artificial neural networks (ANNs) are computational models inspired by biological neural networks. ANNs can process large amounts of inputs to learn from data in a way similar to the human brain. There are different types of ANN architectures including single layer feedforward networks, multilayer feedforward networks, and recurrent networks. ANNs use supervised, unsupervised, or reinforced learning. The backpropagation algorithm is commonly used for training multilayer networks by propagating errors backwards from the output to adjust weights. Developing an ANN application involves collecting data, separating it into training and testing sets, designing the network architecture, initializing parameters/weights, transforming data, training the network using an algorithm like backpropagation, testing performance on new data, and
Entity embeddings for categorical data
Word embeddings are common for NLP tasks, but embeddings can also be used to learn relations among categorical data. Deep learning can be useful also for structured data, and entity embeddings is one reason why it makes sense. These are slides from a seminar held in Sbanken.
Neural network techniques
This document discusses using neural network techniques for sales forecasting. It begins by defining sales forecasting as estimating future demand given certain conditions. It then lists several business areas that benefit from sales forecasting like human resources, R&D, marketing, finance, production, and purchasing. The document outlines the sales forecasting process including setting goals, gathering and analyzing data, applying neural network models, and evaluating outcomes. It then explains key neural network concepts like feed forward, recurrent, and backpropagation models and how they are used for sales forecasting. In particular, it details how backpropagation training algorithms are applied to minimize errors in neural networks.
Deep learning
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
Forecasting of Sales using Neural network techniques
This document discusses using neural network techniques for sales forecasting. It begins by defining sales forecasting and explaining its need in areas like human resources, R&D, marketing, finance, production and purchasing. The document then outlines the sales forecasting process including setting goals, data gathering, analysis, mining, and applying neural network models. It describes the basic concepts of artificial neural networks and different neural network models like feed forward, recurrent, and backpropagation. It provides details on how these models work, especially explaining the backpropagation training algorithm and how it minimizes network error through forward and backward passes. Finally, it lists several references for further information.
Facial Emotion Detection on Children's Emotional Face
The document presents a project on sentiment analysis of human emotions, specifically focusing on detecting emotions from babies' facial expressions using deep learning. It involves loading a facial expression dataset, training a convolutional neural network model to classify 7 emotions (anger, disgust, fear, happy, sad, surprise, neutral), and evaluating the model on test data. An emotion detection application is implemented using the trained model to analyze emotions in real-time images from a webcam with around 60-70% accuracy on random images.
Adaptive synchronous sliding control for a robot manipulator based on neural ...
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
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Similar to nn_important study materoial okfjevh rji
Artificial Neural Network.pptx
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.
Artificial Neural Networks for NIU session 2016 17
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.
Neural network
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each link has a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell : • DENDRITES: Accepts the inputs • SOMA : Process the inputs • AXON : Turns the processed inputs into outputs. • SYNAPSES : The electrochemical contact between the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network are represented by the x1 mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simply summed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): input signal. Weights (Dendrites): determines the importance of incoming value. Output (Axon): output to other neuron or of NN .
neuralnetwork.pptx
This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are:
- Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain.
- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have many applications including voice recognition, image recognition, robotics and more due to their ability to learn from large amounts of data.
neuralnetwork.pptx
This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are:
- Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain.
- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have various applications including voice recognition, image recognition, and robotics due to their ability to learn from large amounts of data.
Artificial Neural Network Learning Algorithm.ppt
This document discusses artificial neural networks (ANN). It describes ANN as computational models inspired by biological neural networks in the brain. ANN consist of interconnected nodes that process information, learn from data, and make predictions by adjusting connection weights. The document outlines different types of ANN architectures including single layer, feedforward, and recurrent networks. It also covers ANN learning techniques such as supervised, unsupervised, reinforcement, and competitive learning.
1 5
The document discusses training and testing neural networks. It covers topics like when a neural network is considered trained based on the application, controlling the training process with learning parameters, an iterative development process to improve the network, avoiding overtraining, and automating parts of the process.
Table of Contents
This project aims to implement a recurrent neural network module for the ACL Neural Toolkit to expand its capabilities. A team will design the neuron and network models, develop a training algorithm, and integrate it into the Toolkit over 5 months. Milestones include preliminary models and algorithms, and incorporating it into the Toolkit to offer a necessary addition that can solve more complex problems than other methods.
Neural Networks.pdf
This document provides an introduction to neural networks, including:
- An overview of neurons and how they are connected in a neural network.
- The main types of neural networks including feedforward, feedback, single layer, and multilayer networks.
- How neural networks learn through supervised, unsupervised, and reinforcement learning methods.
- Common applications of neural networks like pattern recognition, control systems, and forecasting.
- The advantages of neural networks in tasks linear programs cannot do and their ability to learn without reprogramming. Disadvantages include the need for training and high processing requirements for large networks.
Hard & soft computing
Hard computing relies on precise analytical models and computations to solve problems, while soft computing uses approximate techniques inspired by the human brain to provide usable solutions to complex problems. The document then discusses hard computing techniques like algorithms and mathematical formulas. It notes soft computing's advantages like robustness, low cost, and ability to solve complex problems. Soft computing techniques discussed include fuzzy logic, genetic algorithms, and neural networks. Applications mentioned include internet search, robotics, and speech recognition.
Artificial neural network
- An artificial neural network (ANN) is a computational model inspired by biological neural networks in the brain. ANNs contain interconnected nodes that can learn relationships and patterns from data using a process similar to biological learning.
- The basic ANN architecture consists of an input layer, hidden layers, and an output layer. Information flows from the input to output layers through the hidden layers as the network learns.
- There are different types of ANNs that vary in their structure and learning methods, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. ANNs can perform tasks using supervised, unsupervised, or reinforcement learning.
- ANNs have many applications including face recognition, ridesharing, handwriting
Artificial Neural Network
- An artificial neural network (ANN) is a computational model inspired by biological neural networks in the brain. ANNs contain interconnected nodes that can learn relationships and patterns from data through a process of training.
- The basic ANN architecture includes an input layer, hidden layers, and an output layer. Information flows from the input to the output layers through the hidden layers as the network learns.
- There are different types of ANNs that vary in their structure and learning methods, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. ANNs can perform tasks like face recognition, prediction, and classification through supervised, unsupervised, or reinforcement learning.
- While ANNs have advantages like fault tolerance
33.-Multi-Layer-Perceptron.pdf
This document provides an overview of multi-layer perceptrons (MLPs), also known as neural networks. It begins by discussing how perceptrons work, including taking inputs, multiplying them by weights, passing them through an activation function, and producing an output. MLPs consist of multiple stacked perceptron layers that allow them to solve more complex problems. Key aspects that enable deep learning with MLPs include backpropagation to optimize weights, tuning hyperparameters like the number of layers and activation functions, and using advanced training techniques involving learning rates, epochs, batches and optimizer algorithms.
Artificial Neural Network ANN
Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains.
An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold.
Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
Artificial neural network model & hidden layers in multilayer artificial neur...
Artificial neural networks (ANNs) are computational models inspired by biological neural networks. ANNs can process large amounts of inputs to learn from data in a way similar to the human brain. There are different types of ANN architectures including single layer feedforward networks, multilayer feedforward networks, and recurrent networks. ANNs use supervised, unsupervised, or reinforced learning. The backpropagation algorithm is commonly used for training multilayer networks by propagating errors backwards from the output to adjust weights. Developing an ANN application involves collecting data, separating it into training and testing sets, designing the network architecture, initializing parameters/weights, transforming data, training the network using an algorithm like backpropagation, testing performance on new data, and
Entity embeddings for categorical data
Word embeddings are common for NLP tasks, but embeddings can also be used to learn relations among categorical data. Deep learning can be useful also for structured data, and entity embeddings is one reason why it makes sense. These are slides from a seminar held in Sbanken.
Neural network techniques
This document discusses using neural network techniques for sales forecasting. It begins by defining sales forecasting as estimating future demand given certain conditions. It then lists several business areas that benefit from sales forecasting like human resources, R&D, marketing, finance, production, and purchasing. The document outlines the sales forecasting process including setting goals, gathering and analyzing data, applying neural network models, and evaluating outcomes. It then explains key neural network concepts like feed forward, recurrent, and backpropagation models and how they are used for sales forecasting. In particular, it details how backpropagation training algorithms are applied to minimize errors in neural networks.
Deep learning
This document provides an introduction to deep learning. It defines artificial intelligence, machine learning, data science, and deep learning. Machine learning is a subfield of AI that gives machines the ability to improve performance over time without explicit human intervention. Deep learning is a subfield of machine learning that builds artificial neural networks using multiple hidden layers, like the human brain. Popular deep learning techniques include convolutional neural networks, recurrent neural networks, and autoencoders. The document discusses key components and hyperparameters of deep learning models.
Forecasting of Sales using Neural network techniques
This document discusses using neural network techniques for sales forecasting. It begins by defining sales forecasting and explaining its need in areas like human resources, R&D, marketing, finance, production and purchasing. The document then outlines the sales forecasting process including setting goals, data gathering, analysis, mining, and applying neural network models. It describes the basic concepts of artificial neural networks and different neural network models like feed forward, recurrent, and backpropagation. It provides details on how these models work, especially explaining the backpropagation training algorithm and how it minimizes network error through forward and backward passes. Finally, it lists several references for further information.
Facial Emotion Detection on Children's Emotional Face
The document presents a project on sentiment analysis of human emotions, specifically focusing on detecting emotions from babies' facial expressions using deep learning. It involves loading a facial expression dataset, training a convolutional neural network model to classify 7 emotions (anger, disgust, fear, happy, sad, surprise, neutral), and evaluating the model on test data. An emotion detection application is implemented using the trained model to analyze emotions in real-time images from a webcam with around 60-70% accuracy on random images.
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Artificial Neural Networks for NIU session 2016 17
Artificial Neural Networks for NIU session 2016 17
Artificial neural network model & hidden layers in multilayer artificial neur...
Artificial neural network model & hidden layers in multilayer artificial neur...
Forecasting of Sales using Neural network techniques
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Facial Emotion Detection on Children's Emotional Face
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Recently uploaded
Adaptive synchronous sliding control for a robot manipulator based on neural ...
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Advanced control scheme of doubly fed induction generator for wind turbine us...
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
Kinematics 11th jpp- 01. ( Solved ) unacademy namo kaul on 14th may...
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
哪里办理(csu毕业证书)查尔斯特大学毕业证硕士学历原版一模一样
原版一模一样【微信：741003700 】【(csu毕业证书)查尔斯特大学毕业证硕士学历】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
22CYT12-Unit-V-E Waste and its Management.ppt
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Low power architecture of logic gates using adiabatic techniques
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
DfMAy 2024 - key insights and contributions
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
原版制作(unimelb毕业证书)墨尔本大学毕业证Offer一模一样
学校原件一模一样【微信：741003700 】《(unimelb毕业证书)墨尔本大学毕业证》【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
bank management system in java and mysql report1.pdf
truth is high but higher still is truth full living
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Adaptive synchronous sliding control for a robot manipulator based on neural ...
Adaptive synchronous sliding control for a robot manipulator based on neural ...
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Advanced control scheme of doubly fed induction generator for wind turbine us...
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
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CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECT
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
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Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
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bank management system in java and mysql report1.pdf
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nn_important study materoial okfjevh rji
- 2. Contents .Introduction to Neural Network .Neurons .Activation Function .Types of Neural Network .Learning In Neural Networks .Application of Neural Network .Advantages of Neural Network .Disadvantages Of Neural Network
- 3. Neural Networks • A method ofcomputing, based on the interactionof multipleconnected processing elements. • A powerful technique to solve many real world problems. • The ability to learn from experience in order to improve their performance. • Ability to deal with incomplete information
- 4. • • • • Biological approach to AI Developed in 1943 Comprised of one or more layers of neurons Several types, we’ll focus on feed-forward and feedback networks Basics Of Neural Network
- 6. Neural Network Neurons • • • • Receives n-inputs Multiplies each input by its weight Applies activation function to the sum of results Outputs result
- 7. Activation Functions • • • Controlswhen unit is “active” or “inactive” Threshold function outputs 1 when input is positive and 0 otherwise Sigmoid function = 1 / (1 + e-x)
- 8. Neural Network types can be classified based on following attributes: • Connection Type - Static (feedforward) - Dynamic (feedback) • Topology - Single layer - Multilayer - Recurrent • Learning Methods - Supervised - Unsupervised - Reinforcement Types of Neural Networks
- 9. Classification Based On Connection Types (unit delay operator z-1 implies dynamic system) •Static(Feedforward) • Dynamic(Feedback) Classification Based On Topology • Single layer • Multilayer • Recurrent
- 10. Classification Based On Learning Method • Supervised • Unsupervised • Reinforcement Supervised learning • Each training pattern: input + desired output • At each presentation: adapt weights • Aftermany epochs convergence to a local minimum
- 11. Unsupervised Learning • • • • • No help from the outside No training data,no information availableon the desired output Learning by doing Used to pick out structure in the input: • Clustering • Reduction of dimensionality compression Example: Kohonen’s Learning Law
- 12. Reinforcement learning • • • • Teacher: training data The teacher scores the performance of the training examples Use performance score to shuffle weights ‘randomly’ Relatively slow learning due to ‘randomness’
- 13. Neural Network Applications • Pattern recognition • Investment analysis • Control systems & monitoring • Mobile computing • Marketing and financial applications • Forecasting – sales, market research, meteorology
- 14. Advantages: • A neural network can perform tasks that a linear program can not. • When anelementoftheneural network fails, it can continue withoutany problemby their parallel nature. • A neural network learns and does not need tobe reprogrammed. • It can be implemented in any application. • It can be implemented without any problem
- 15. Disadvantages: • Theneural network needs training to operate. • The architectureof a neural network isdifferentfrom the architecture of microprocessors therefore needs to beemulated. • Requires highprocessing time for large neural networks.
- 16. Conclusions • • • Neural networks provide ability to provide more human-like AI Takes rough approximation and hard-coded reactions out of AI design (i.e. Rules and FSMs) Still require a lot of fine- tuning during development
- 17. References • • • • • Neural Networks, Fuzzy Logic, and Genetic Algorithm ( synthesis and Application) S.Rajasekaran, G.A. Vijayalakshmi Pai, PHI Neuro Fuzzy and Soft Computing, J. S. R. JANG,C.T. Sun, E. Mitzutani, PHI Neural Netware, a tutorial on neural networks Sweetser, Penny. “Strategic Decision-Making with Neural Networks and Influence Maps”, AI Game Programming Wisdom 2, Section 7.7 (439 – 46) Russell, Stuart and Norvig, Peter. Artificial Intelligence: A Modern Approach, Section 20.5 (736 – 48)
- 18. THANK YOU
- 19. QUESTIONS ?