nil-100128213838-phpapp02.pdf
•
0 likes•11 views
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.
Report
Share
Report
Share
Download to read offline
Recommended
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.
nn_important study materoial okfjevh rji
this is nn hwechqewioenwec ewcjoqewc ew ewc ew ewjoce cipo jwe h ewoce eoerijoc jerjew weioew ewd qewodjqe ci ew dew de ew wd fj weo wejfwe f weijeifiwj oewcjhcp wjdmwenf wjwijdqewiof jwoefjiofjqoef jwejfioqwfe wqefpjewqijewe weifj ewfiwjfpwef weqfojewoef qefqewfew jpopqwefhqwejfowq ewf weofjwioqfe wefoijwepfoih ewf w few fjwo wef wef ew fjp[ jwe ffjqew fjqe qwe[f jwefewhfp qdwpfheq0ef qwefqeiwfhq0wdchqdfv jierjfioheq erjfiojerfewf jwiewfjoqejheq qwewioiqewofjqeowfqefhdweew nfigierfgbr wefqefeferfpo j djwfjcwpc ewefjioef wejfp
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.
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.
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.
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.
Recommended
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.
nn_important study materoial okfjevh rji
this is nn hwechqewioenwec ewcjoqewc ew ewc ew ewjoce cipo jwe h ewoce eoerijoc jerjew weioew ewd qewodjqe ci ew dew de ew wd fj weo wejfwe f weijeifiwj oewcjhcp wjdmwenf wjwijdqewiof jwoefjiofjqoef jwejfioqwfe wqefpjewqijewe weifj ewfiwjfpwef weqfojewoef qefqewfew jpopqwefhqwejfowq ewf weofjwioqfe wefoijwepfoih ewf w few fjwo wef wef ew fjp[ jwe ffjqew fjqe qwe[f jwefewhfp qdwpfheq0ef qwefqeiwfhq0wdchqdfv jierjfioheq erjfiojerfewf jwiewfjoqejheq qwewioiqewofjqeowfqefhdweew nfigierfgbr wefqefeferfpo j djwfjcwpc ewefjioef wejfp
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.
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.
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.
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.
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.
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.
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
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 .
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 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.
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.
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.
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
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.
Ch 1-1 introduction
اسلایدهای درس شبکه عصبی و یادگیری عمیق که در دانشگاه شیراز توسط استاد اقبال منصوری تدریس می شود.
Neural network and deep learning course slide taught by Professor Iqbal Mansouri at Shiraz University.
Neural Networks-1
This document provides an overview of three types of machine learning: supervised learning, reinforcement learning, and unsupervised learning. It then discusses supervised learning in more detail, explaining that each training case consists of an input and target output. Regression aims to predict a real number output, while classification predicts a class label. The learning process typically involves choosing a model and adjusting its parameters to reduce the discrepancy between the model's predicted output and the true target output on each training case.
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.
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.
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.
Computer Design Concepts for Machine Learning
In this talk, after a brief overview of AI concepts in particular Machine Learning (ML) techniques, some of the well-known computer design concepts for high performance and power efficiency are presented. Subsequently, those techniques that have had a promising impact for computing ML algorithms are discussed. Deep learning has emerged as a game changer for many applications in various fields of engineering and medical sciences. Although the primary computation function is matrix vector multiplication, many competing efficient implementations of this primary function have been proposed and put into practice. This talk will review and compare some of those techniques that are used for ML computer design.
Neural Networks and Deep Learning Basics
This document provides an introduction to deep learning and neural networks. It discusses:
- Deep learning learns representations of data rather than relying on hand-engineered features.
- Deep learning architectures include neural networks, convolutional neural networks, and recurrent neural networks.
- Deep learning represents concepts in a nested hierarchy from simple to more abstract, with each layer learning slightly more complex representations. This allows it to learn its own feature detectors from raw data.
Artificial Neural Network
An artificial neural network (ANN) is a computational model inspired by the human brain that can learn from large amounts of data to detect patterns and relationships. ANNs are formed from hundreds of artificial neurons connected by coefficients that are organized in layers. The power of ANNs comes from connecting neurons, with each neuron consisting of a weighted input, transfer function, and single output. ANNs learn by adjusting the weights between neurons to minimize error and reach a specified level of accuracy when trained on data. Once trained, ANNs can be used to make predictions on new input data.
CHAPTER 02 - Linear codes.ppt
Linear codes are special sets of words of a fixed length over an alphabet where the words can be treated as vectors. Linear codes have important properties including a concise description, nice mathematical properties, and efficient encoding and decoding. A linear code is a subset of the vector space of all possible words where the sum of any two codewords is also a codeword, and where codewords can be multiplied by field elements and remain codewords. Examples of properties of linear codes include that their minimum distance is equal to the smallest weight of a non-zero codeword, and that they have a unique encoding for each message using a generator matrix.
CHAPTER 02 - Linear codes.ppt
Linear codes are special sets of words of a fixed length over an alphabet where the words can be treated as vectors. Linear codes have important properties including a concise description, nice mathematical properties, and efficient encoding and decoding. A linear code is a subset of words where the sum of any two codewords is also a codeword, and where codewords can be multiplied by field elements and remain codewords. Common operations on linear codes include finding equivalent codes through row and column operations, encoding by matrix multiplication, and decoding using coset leaders.
More Related Content
Similar to nil-100128213838-phpapp02.pdf
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.
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.
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
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 .
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 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.
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.
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.
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
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.
Ch 1-1 introduction
اسلایدهای درس شبکه عصبی و یادگیری عمیق که در دانشگاه شیراز توسط استاد اقبال منصوری تدریس می شود.
Neural network and deep learning course slide taught by Professor Iqbal Mansouri at Shiraz University.
Neural Networks-1
This document provides an overview of three types of machine learning: supervised learning, reinforcement learning, and unsupervised learning. It then discusses supervised learning in more detail, explaining that each training case consists of an input and target output. Regression aims to predict a real number output, while classification predicts a class label. The learning process typically involves choosing a model and adjusting its parameters to reduce the discrepancy between the model's predicted output and the true target output on each training case.
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.
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.
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.
Computer Design Concepts for Machine Learning
In this talk, after a brief overview of AI concepts in particular Machine Learning (ML) techniques, some of the well-known computer design concepts for high performance and power efficiency are presented. Subsequently, those techniques that have had a promising impact for computing ML algorithms are discussed. Deep learning has emerged as a game changer for many applications in various fields of engineering and medical sciences. Although the primary computation function is matrix vector multiplication, many competing efficient implementations of this primary function have been proposed and put into practice. This talk will review and compare some of those techniques that are used for ML computer design.
Neural Networks and Deep Learning Basics
This document provides an introduction to deep learning and neural networks. It discusses:
- Deep learning learns representations of data rather than relying on hand-engineered features.
- Deep learning architectures include neural networks, convolutional neural networks, and recurrent neural networks.
- Deep learning represents concepts in a nested hierarchy from simple to more abstract, with each layer learning slightly more complex representations. This allows it to learn its own feature detectors from raw data.
Artificial Neural Network
An artificial neural network (ANN) is a computational model inspired by the human brain that can learn from large amounts of data to detect patterns and relationships. ANNs are formed from hundreds of artificial neurons connected by coefficients that are organized in layers. The power of ANNs comes from connecting neurons, with each neuron consisting of a weighted input, transfer function, and single output. ANNs learn by adjusting the weights between neurons to minimize error and reach a specified level of accuracy when trained on data. Once trained, ANNs can be used to make predictions on new input data.
Similar to nil-100128213838-phpapp02.pdf (20)
Unit one ppt of deeep learning which includes Ann cnn
Unit one ppt of deeep learning which includes Ann cnn
Artificial neural network model & hidden layers in multilayer artificial neur...
Artificial neural network model & hidden layers in multilayer artificial neur...
Facial Emotion Detection on Children's Emotional Face
Facial Emotion Detection on Children's Emotional Face
More from dlakmlkfma
CHAPTER 02 - Linear codes.ppt
Linear codes are special sets of words of a fixed length over an alphabet where the words can be treated as vectors. Linear codes have important properties including a concise description, nice mathematical properties, and efficient encoding and decoding. A linear code is a subset of the vector space of all possible words where the sum of any two codewords is also a codeword, and where codewords can be multiplied by field elements and remain codewords. Examples of properties of linear codes include that their minimum distance is equal to the smallest weight of a non-zero codeword, and that they have a unique encoding for each message using a generator matrix.
CHAPTER 02 - Linear codes.ppt
Linear codes are special sets of words of a fixed length over an alphabet where the words can be treated as vectors. Linear codes have important properties including a concise description, nice mathematical properties, and efficient encoding and decoding. A linear code is a subset of words where the sum of any two codewords is also a codeword, and where codewords can be multiplied by field elements and remain codewords. Common operations on linear codes include finding equivalent codes through row and column operations, encoding by matrix multiplication, and decoding using coset leaders.
intro (1).ppt
This document provides an overview of the CSCE 613: Fundamentals of VLSI Chip Design course. The course focuses on using electronic design automation (EDA) tools to manage the complexity of designing very large scale integrated (VLSI) circuits. It will cover topics like CMOS circuit design, standard cell library development, VHDL design, logic synthesis, and place-and-route. Students will learn industry-standard EDA tools from Cadence and Synopsys through tutorials and hands-on projects. The goal is for students to gain experience applying EDA methodologies to large-scale digital integrated circuit design.
Review-other-MDS-changes.pptx
The document provides information on changes to the MDS (Minimum Data Set) for October 2019, including changes made to several sections and items. Chapter 2 was extensively revised and individual changes were not tracked. Cognition assessment is now required for all PPS assessments. For the Interim Payment Assessment, Section GG covers the last 3 days. The HIPPS code under PDPM includes classification codes for each component and an assessment indicator. Section K no longer includes mechanically altered diets, and respite care was removed from Section O.
Presentation (2).pptx
The document discusses the key components of a data communication system:
1. A message is the data or information being communicated between sender and receiver.
2. A sender is the device that originates the transmission of a message, such as a computer, phone, or camera.
3. A receiver is the destination device that receives the message, such as another computer, phone, or workstation.
4. A transmission medium is the physical path that allows data transmission between sender and receiver, either through wired or wireless means like cables, radio waves, or microwaves.
5. Protocols are the set of rules that govern data communication and allow different devices to connect and understand each other.
cloud computing.pptx
The document discusses cloud computing, which is the on-demand delivery of IT resources over the Internet through pay-as-you-go pricing. Rather than owning physical servers and data centers, companies can access computing power, storage, databases, and other technologies from cloud providers like AWS. Some benefits of cloud computing include competitive advantages through faster access to latest applications, quick deployment of services in fewer clicks, and cost savings by paying only for resources used rather than maintaining physical infrastructure. Cloud storage also enables easier backup and recovery of data.
cloud computing.pptx
The document discusses cloud computing, which is the on-demand delivery of IT resources over the Internet through pay-as-you-go pricing. Rather than owning physical servers and data centers, companies can access computing power, storage, databases, and other technologies from cloud providers like AWS. Some benefits of cloud computing include competitive advantages through faster access to latest applications, quick deployment of services in fewer clicks, and cost savings by paying only for resources used rather than maintaining physical infrastructure. Cloud storage also enables easier backup and recovery of data.
ch10_2_v1.ppt
This document discusses error detection and correction techniques used in digital communications. It covers Hamming codes, cyclic redundancy checks (CRCs), checksums, and linear block codes. Key points include:
- Hamming codes can detect up to two errors and correct single errors by ensuring a minimum Hamming distance of 3 between codewords.
- CRCs use cyclic codes to detect errors by calculating syndromes based on polynomial division of the received codeword by a generator polynomial.
- Checksums involve sending the sum of data words along with the words to detect errors by verifying the received sum is correct.
- Linear block codes allow the XOR of any two valid codewords to produce another valid codeword.
ch10_2_v1.ppt
This document discusses error detection and correction techniques for digital communications. It covers Hamming codes, cyclic redundancy checks (CRCs), checksums, and other coding schemes. Key points include:
- Hamming codes can detect up to two errors and correct single errors by ensuring a minimum Hamming distance of 3 between codewords.
- CRCs use cyclic codes to detect errors, with the CRC value serving as a checksum. CRC division and hardware implementations are examined.
- Cyclic codes have the property that cyclically shifting a codeword results in another valid codeword. This allows for efficient encoding and decoding.
- Checksums involve sending the sum of message bits along with the message for the receiver to
ch10_2_v1.ppt
This document discusses error detection and correction techniques using Hamming codes and cyclic redundancy checks (CRCs). It explains key concepts like Hamming distance, minimum distance, linear block codes, and cyclic codes. Examples are provided to illustrate how Hamming codes can detect up to two errors and correct one error using a minimum distance of 3. CRC codes are also examined, showing how they use polynomial division to detect errors. The advantages of cyclic codes for hardware implementation are noted.
Entrepreneurship 1.pptx
This document discusses small and medium enterprises (SMEs) including definitions, classifications, and legal boundaries around the world. SMEs are defined as businesses with personnel or employment below certain limits that vary by country. They make up the vast majority of businesses worldwide and are an important source of employment. The document outlines SME classifications and limits in various countries and regions including India, Egypt, Africa, Bangladesh and Asia. SMEs are classified based on factors like investment amount, turnover, number of employees, and fixed assets.
THE IMPACT OF SOCIAL MEDIA ON YOUNG GENERATION.pptx
The document discusses social media and its use and impact. It defines social media as virtual communication networks that allow people to create and share information. It then lists popular social media platforms and discusses how people use social media to connect with others, participate in events, build their image, and stay informed. The document also provides statistics on social media use in India and some of the benefits and drawbacks of social media, particularly for youth.
embeddedsystem-190202172328.pdf
A system is a collection of components that work together for a common purpose. An embedded system is a combination of computer hardware and software designed for a specific function within a larger system. Embedded systems have components like a power supply, processor, memory, timers/counters, communication ports, input/output ports, and application-specific circuits. They are used in applications like watches, washing machines, digital devices, appliances, and more.
More from dlakmlkfma (13)
THE IMPACT OF SOCIAL MEDIA ON YOUNG GENERATION.pptx
THE IMPACT OF SOCIAL MEDIA ON YOUNG GENERATION.pptx
Recently uploaded
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.
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Dynamic Programming
Backtracking
Techniques for Graphs
Branch and Bound
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
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.
PROJECT FORMAT FOR EVS AMITY UNIVERSITY GWALIOR.ppt
Ppt on evs amity University project work.........................??????!?!?!?!!?!!?!!?/?/?/?/?/?
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.
Technical Drawings introduction to drawing of prisms
Method of technical Drawing of prisms,and cylinders.
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.
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.
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
Kinematics 11th jpp- 01. ( Solved ) unacademy namo kaul on 14th may...
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Recently uploaded (20)
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Design and Analysis of Algorithms-DP,Backtracking,Graphs,B&B
Generative AI leverages algorithms to create various forms of content
Generative AI leverages algorithms to create various forms of content
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressions
basic-wireline-operations-course-mahmoud-f-radwan.pdf
basic-wireline-operations-course-mahmoud-f-radwan.pdf
PROJECT FORMAT FOR EVS AMITY UNIVERSITY GWALIOR.ppt
PROJECT FORMAT FOR EVS AMITY UNIVERSITY GWALIOR.ppt
Advanced control scheme of doubly fed induction generator for wind turbine us...
Advanced control scheme of doubly fed induction generator for wind turbine us...
Technical Drawings introduction to drawing of prisms
Technical Drawings introduction to drawing of prisms
Low power architecture of logic gates using adiabatic techniques
Low power architecture of logic gates using adiabatic techniques
Tutorial for 16S rRNA Gene Analysis with QIIME2.pdf
Tutorial for 16S rRNA Gene Analysis with QIIME2.pdf
Adaptive synchronous sliding control for a robot manipulator based on neural ...
Adaptive synchronous sliding control for a robot manipulator based on neural ...
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
[JPP-1] - (JEE 3.0) - Kinematics 1D - 14th May..pdf
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
nil-100128213838-phpapp02.pdf
- 1. Neural Network Guided by Chapala Maharana Submitted By- Nilmani Singh Branch-CSE Regd. No.-0601289251
- 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 of computing, based on the interaction of multiple connected 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 • Controls when 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 •Static(Feedforward) (unit delay operator z-1 implies dynamic system) Classification Based On Topology •Dynamic(Feedback) •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 • After many epochs convergence to a local minimum
- 11. Unsupervised Learning • No help from the outside • No training data, no information available on 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. • Pattern recognition • Investment analysis • Control systems & monitoring • Mobile computing • Marketing and financial applications • Forecasting – sales, market research, meteorology Neural Network Applications
- 14. Advantages: •A neural network can perform tasks that a linear program can not. •When an element of the neural network fails, it can continue without any problem by their parallel nature. •A neural network learns and does not need to be reprogrammed. •It can be implemented in any application. •It can be implemented without any problem
- 15. Disadvantages: •The neural network needs training to operate. •The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated. •Requires high processing 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)