This document discusses the perceptron model and learning algorithm. It begins with an overview of perceptrons as the first neural network model from the 1960s and how they work as single-layer classifiers. It then describes the perceptron node and threshold logic unit in more detail. The rest of the document works through an example of the perceptron learning algorithm, showing how the weights are updated on each training instance to minimize errors. It concludes with discussions of linear separability and the limited functionality of perceptrons as single-layer classifiers.
This document discusses multi-layer perceptrons and neural networks. It covers threshold logic units, activation functions, training algorithms like perceptron learning rule and gradient descent, and applications of neural networks like object recognition and time series analysis. It also discusses concepts like generalization to prevent overfitting neural networks to training data.
An artificial neuron network (neural network) is a computational model that mimics the way nerve cells work in the human brain. Artificial neural networks (ANNs) use learning algorithms that can independently make adjustments - or learn, in a sense - as they receive new input
This document provides an overview of machine learning and artificial intelligence concepts. It begins with definitions of learning and machine learning. Machine learning is described as programming computers to optimize performance using example data. Common machine learning algorithms like linear regression, naive Bayes, decision trees, and k-means clustering are discussed along with applications. Artificial neural networks and the perceptron learning algorithm are explained in detail. The key point is that perceptron learning will always find weights to correctly classify inputs if a linear solution exists.
The document discusses perceptrons and artificial neural networks, including describing the architecture and learning algorithm of a simple perceptron for pattern classification, providing an example of how the perceptron learns from labeled training data to determine the weights and bias that allow it to classify new input patterns, and assigning exercises to modify the perceptron code provided to classify different input data and analyze its convergence over training epochs.
Regression models the relationship between continuous variables by fitting a line or curve to the data points. Logistic regression performs nonlinear regression by first transforming the dependent variable values to logits (log odds) and then fitting a linear regression line to the transformed data. This results in a sigmoid curve that models the probability of an output variable given continuous input variables. The sigmoid curve bounds the predicted probabilities between 0 and 1, allowing logistic regression to be used for binary classification problems.
The document discusses linear regression and logistic regression. Linear regression finds the best-fitting linear relationship between independent and dependent variables. Logistic regression applies a sigmoid function to the linear combination of inputs to output a probability between 0 and 1, fitting a logistic curve rather than a straight line. It works by first transforming the probabilities into log-odds (logits) and then performing linear regression on the transformed data. This allows predicting probabilities while ensuring outputs remain between 0 and 1.
This document discusses multi-layer perceptrons and neural networks. It covers threshold logic units, activation functions, training algorithms like perceptron learning rule and gradient descent, and applications of neural networks like object recognition and time series analysis. It also discusses concepts like generalization to prevent overfitting neural networks to training data.
An artificial neuron network (neural network) is a computational model that mimics the way nerve cells work in the human brain. Artificial neural networks (ANNs) use learning algorithms that can independently make adjustments - or learn, in a sense - as they receive new input
This document provides an overview of machine learning and artificial intelligence concepts. It begins with definitions of learning and machine learning. Machine learning is described as programming computers to optimize performance using example data. Common machine learning algorithms like linear regression, naive Bayes, decision trees, and k-means clustering are discussed along with applications. Artificial neural networks and the perceptron learning algorithm are explained in detail. The key point is that perceptron learning will always find weights to correctly classify inputs if a linear solution exists.
The document discusses perceptrons and artificial neural networks, including describing the architecture and learning algorithm of a simple perceptron for pattern classification, providing an example of how the perceptron learns from labeled training data to determine the weights and bias that allow it to classify new input patterns, and assigning exercises to modify the perceptron code provided to classify different input data and analyze its convergence over training epochs.
Regression models the relationship between continuous variables by fitting a line or curve to the data points. Logistic regression performs nonlinear regression by first transforming the dependent variable values to logits (log odds) and then fitting a linear regression line to the transformed data. This results in a sigmoid curve that models the probability of an output variable given continuous input variables. The sigmoid curve bounds the predicted probabilities between 0 and 1, allowing logistic regression to be used for binary classification problems.
The document discusses linear regression and logistic regression. Linear regression finds the best-fitting linear relationship between independent and dependent variables. Logistic regression applies a sigmoid function to the linear combination of inputs to output a probability between 0 and 1, fitting a logistic curve rather than a straight line. It works by first transforming the probabilities into log-odds (logits) and then performing linear regression on the transformed data. This allows predicting probabilities while ensuring outputs remain between 0 and 1.
This document discusses the Perceptron algorithm for linear classification. It begins with an overview of linear classifiers and the Perceptron, then describes the Perceptron algorithm which makes weight updates only for misclassified examples. The algorithm is guaranteed to converge if the data is linearly separable. The document also discusses the Perceptron mistake bound theorem, which provides an upper bound on the number of mistakes the algorithm can make based on the margin of separation between classes.
Episode 50 : Simulation Problem Solution Approaches Convergence Techniques S...SAJJAD KHUDHUR ABBAS
Episode 50 : Simulation Problem Solution Approaches Convergence Techniques Simulation Strategies
3.2.3.3. Quasi-Newton (QN) Methods
These methods represent a very important class of techniques because of their extensive use in practical alqorithms. They attempt to use an approximation to the Jacobian and then update this at each step thus reducing the overall computational work.
The QN method uses an approximation Hk to the true Jacobian i and computes the step via a Newton-like iteration. That is,
SAJJAD KHUDHUR ABBAS
Ceo , Founder & Head of SHacademy
Chemical Engineering , Al-Muthanna University, Iraq
Oil & Gas Safety and Health Professional – OSHACADEMY
Trainer of Trainers (TOT) - Canadian Center of Human
Development
This document provides an overview of linear classifiers and support vector machines (SVMs) for text classification. It explains that SVMs find the optimal separating hyperplane between classes by maximizing the margin between the hyperplane and the closest data points of each class. The document discusses how SVMs can be extended to non-linear classification through kernel methods and feature spaces. It also provides details on solving the SVM optimization problem and using SVMs for classification.
This document discusses machine learning classification using a single layer feed forward neural network. It begins with definitions of machine learning and the different types of machine learning problems. Supervised learning classification is explained where the goal is to learn from labeled training data to classify new observations. Common classification algorithms and the components of a learning model are described. Finally, the document provides an example of how a single layer perceptron neural network can be used to classify a sample dataset into two classes by learning the optimal weights through an iterative process.
The document provides an overview of neural networks and discusses:
- The background of neural networks including how they relate to brains and computational models.
- Abstractions of neurons and neural networks including McCullough-Pitts neurons and their representational completeness.
- Learnability in perceptrons including the perceptron training rule and convergence theorem for a perceptron to learn functions that are representable.
- An example of perceptron learning is shown for an "AND" function.
A neural network maps a set of inputs to a set of outputs. It is composed of nodes or units connected by links with weights. A neural network can compute or approximate functions, perform pattern recognition, signal processing, and learn to do any of these. A perceptron is a basic type of neural network that uses a threshold activation function. It can be trained to learn functions using the perceptron learning rule, which adjusts the weights to minimize errors between the network's output and the target output.
This document discusses different types of neural networks including simple neural networks, perceptrons, and ADALINE networks. It provides details on activation functions, learning rules like Hebbian learning, perceptron learning rule, delta rule, and competitive learning rule. It also discusses characteristics of neural networks like mapping capabilities, learning by examples, generalization, and parallel processing. Examples are given to demonstrate training of perceptrons and ADALINE networks.
This document describes self-organizing maps and adaptive resonance theory neural networks. It discusses how self-organizing maps use competitive learning and weight adjustment to have neurons represent different input classes. Adaptive resonance theory networks combine self-organizing maps with associative (outstar) networks so the input layer finds the most similar stored pattern and the output layer recalls the full pattern. The adaptive resonance algorithm compares input and output patterns using an AND operation and vigilance threshold to determine if the weight adjustments should be made or if a new neuron is needed to represent the input.
This document outlines a course on neural networks and fuzzy systems. The course is divided into two parts, with part one focusing on neural networks over 11 weeks, covering topics like perceptrons, multi-layer feedforward networks, and unsupervised learning. Part two focuses on fuzzy systems over 4 weeks, covering fuzzy set theory and fuzzy systems. The document also provides details on concepts like linear separability, decision boundaries, perceptron learning algorithms, and using neural networks to solve problems like AND, OR, and XOR gates.
This document provides an overview of support vector machines (SVMs) for machine learning. It explains that SVMs find the optimal separating hyperplane that maximizes the margin between examples of separate classes. This is achieved by formulating SVM training as a convex optimization problem that can be solved efficiently. The document discusses how SVMs can handle non-linear decision boundaries using the "kernel trick" to implicitly map examples to higher-dimensional feature spaces without explicitly performing the mapping.
The document provides an overview of artificial neural networks (ANNs) and the perceptron learning algorithm. It discusses how biological neurons inspire ANNs and how a basic perceptron works using a simple example with inputs, weights, and outputs. The perceptron learning algorithm is then explained, which updates weights based on whether the perceptron's prediction was correct or incorrect on each training example. Finally, the document introduces multilayer perceptrons which can solve non-linearly separable problems by connecting multiple perceptron layers together through a process called backpropagation.
Data science involves extracting insights from large volumes of data. It is an interdisciplinary field that uses techniques from statistics, machine learning, and other domains. The document provides examples of classification algorithms like k-nearest neighbors, naive Bayes, and perceptrons that are commonly used in data science to build models for tasks like spam filtering or sentiment analysis. It also discusses clustering, frequent pattern mining, and other machine learning concepts.
This document provides an overview of single layer perceptrons and their use in classification tasks. It discusses the McCulloch-Pitts neuron model and how networks of these neurons can be connected to implement logic gates. It then introduces the perceptron as a single layer feedforward network and describes how to train perceptrons using supervised learning and the perceptron learning rule to classify data. Finally, it provides an example classification task of distinguishing trucks using their mass and length attributes.
Machine Learning: The Bare Math Behind LibrariesJ On The Beach
During this presentation, we will answer how much you’ll need to invest in a superhero costume to be as popular as Superman. We will generate a unique logo which will stand against the ever popular Batman and create new superhero teams. We shall achieve it using linear regression and neural networks.
Machine learning is one of the hottest buzzwords in technology today as well as one of the most innovative fields in computer science – yet people use libraries as black boxes without basic knowledge of the field. In this session, we will strip them to bare math, so next time you use a machine learning library, you’ll have a deeper understanding of what lies underneath.
During this session, we will first provide a short history of machine learning and an overview of two basic teaching techniques: supervised and unsupervised learning.
We will start by defining what machine learning is and equip you with an intuition of how it works. We will then explain the gradient descent algorithm with the use of simple linear regression to give you an even deeper understanding of this learning method. Then we will project it to supervised neural networks training.
Within unsupervised learning, you will become familiar with Hebb’s learning and learning with concurrency (winner takes all and winner takes most algorithms). We will use Octave for examples in this session; however, you can use your favourite technology to implement presented ideas.
Our aim is to show the mathematical basics of neural networks for those who want to start using machine learning in their day-to-day work or use it already but find it difficult to understand the underlying processes. After viewing our presentation, you should find it easier to select parameters for your networks and feel more confident in your selection of network type, as well as be encouraged to dive into more complex and powerful deep learning methods.
This document provides an introduction to support vector machines (SVMs) for text classification. It discusses how SVMs find an optimal separating hyperplane that maximizes the margin between classes. SVMs can handle non-linear classification through the use of kernels, which map data into a higher-dimensional feature space. The document outlines the mathematical formulations of linear and soft-margin SVMs, explains how the kernel trick allows evaluating inner products implicitly in that feature space, and summarizes how SVMs are used for classification tasks.
This document provides an overview of optimization techniques. It defines optimization as identifying variable values that minimize or maximize an objective function subject to constraints. It then discusses various applications of optimization in finance, engineering, and data modeling. The document outlines different types of optimization problems and algorithms. It provides examples of unconstrained optimization algorithms like gradient descent, conjugate gradient, Newton's method, and BFGS. It also discusses the Nelder-Mead simplex algorithm for constrained optimization and compares the performance of these algorithms on sample problems.
This document provides an overview of a single variable calculus course taught by Dr. Abdulla Al-Othman. The course covers differentiation and integration, including key ideas, theory, proofs of rules and theorems, and examples of applying differentiation and integration to polynomials, trigonometric, exponential, and logarithmic functions. The document also briefly introduces differential equations and techniques for solving first order ordinary differential equations.
Two algorithms to accelerate training of back-propagation neural networksESCOM
This document proposes two algorithms to initialize the weights of neural networks to accelerate training. Algorithm I performs a step-by-step orthogonalization of the input matrices to drive them towards a diagonal form, aiming to place the network closer to the convergence points of the activation function. Algorithm II aims to jointly diagonalize the input matrices to also drive them towards a diagonal form. The algorithms are shown to significantly reduce training time compared to random initialization, though Algorithm I works best when the activation function has φ(0)>1.
The document discusses various greedy algorithms including knapsack problems, minimum spanning trees, shortest path algorithms, and job sequencing. It provides descriptions of greedy algorithms, examples to illustrate how they work, and pseudocode for algorithms like fractional knapsack, Prim's, Kruskal's, Dijkstra's, and job sequencing. Key aspects covered include choosing the best option at each step and building up an optimal solution incrementally using greedy choices.
Self-organizing networks can perform unsupervised clustering by mapping high-dimensional input patterns into a smaller number of clusters in output space through competitive learning. Fixed weight competitive networks like Maxnet, Mexican Hat net, and Hamming net use competitive learning with fixed weights. Maxnet uses winner-take-all competition to select the neuron whose weights best match the input. Mexican Hat net has both excitatory and inhibitory connections between neurons to enhance contrast. Hamming net determines which exemplar vector most closely matches the input using the Hamming distance measure.
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.
This document discusses the Perceptron algorithm for linear classification. It begins with an overview of linear classifiers and the Perceptron, then describes the Perceptron algorithm which makes weight updates only for misclassified examples. The algorithm is guaranteed to converge if the data is linearly separable. The document also discusses the Perceptron mistake bound theorem, which provides an upper bound on the number of mistakes the algorithm can make based on the margin of separation between classes.
Episode 50 : Simulation Problem Solution Approaches Convergence Techniques S...SAJJAD KHUDHUR ABBAS
Episode 50 : Simulation Problem Solution Approaches Convergence Techniques Simulation Strategies
3.2.3.3. Quasi-Newton (QN) Methods
These methods represent a very important class of techniques because of their extensive use in practical alqorithms. They attempt to use an approximation to the Jacobian and then update this at each step thus reducing the overall computational work.
The QN method uses an approximation Hk to the true Jacobian i and computes the step via a Newton-like iteration. That is,
SAJJAD KHUDHUR ABBAS
Ceo , Founder & Head of SHacademy
Chemical Engineering , Al-Muthanna University, Iraq
Oil & Gas Safety and Health Professional – OSHACADEMY
Trainer of Trainers (TOT) - Canadian Center of Human
Development
This document provides an overview of linear classifiers and support vector machines (SVMs) for text classification. It explains that SVMs find the optimal separating hyperplane between classes by maximizing the margin between the hyperplane and the closest data points of each class. The document discusses how SVMs can be extended to non-linear classification through kernel methods and feature spaces. It also provides details on solving the SVM optimization problem and using SVMs for classification.
This document discusses machine learning classification using a single layer feed forward neural network. It begins with definitions of machine learning and the different types of machine learning problems. Supervised learning classification is explained where the goal is to learn from labeled training data to classify new observations. Common classification algorithms and the components of a learning model are described. Finally, the document provides an example of how a single layer perceptron neural network can be used to classify a sample dataset into two classes by learning the optimal weights through an iterative process.
The document provides an overview of neural networks and discusses:
- The background of neural networks including how they relate to brains and computational models.
- Abstractions of neurons and neural networks including McCullough-Pitts neurons and their representational completeness.
- Learnability in perceptrons including the perceptron training rule and convergence theorem for a perceptron to learn functions that are representable.
- An example of perceptron learning is shown for an "AND" function.
A neural network maps a set of inputs to a set of outputs. It is composed of nodes or units connected by links with weights. A neural network can compute or approximate functions, perform pattern recognition, signal processing, and learn to do any of these. A perceptron is a basic type of neural network that uses a threshold activation function. It can be trained to learn functions using the perceptron learning rule, which adjusts the weights to minimize errors between the network's output and the target output.
This document discusses different types of neural networks including simple neural networks, perceptrons, and ADALINE networks. It provides details on activation functions, learning rules like Hebbian learning, perceptron learning rule, delta rule, and competitive learning rule. It also discusses characteristics of neural networks like mapping capabilities, learning by examples, generalization, and parallel processing. Examples are given to demonstrate training of perceptrons and ADALINE networks.
This document describes self-organizing maps and adaptive resonance theory neural networks. It discusses how self-organizing maps use competitive learning and weight adjustment to have neurons represent different input classes. Adaptive resonance theory networks combine self-organizing maps with associative (outstar) networks so the input layer finds the most similar stored pattern and the output layer recalls the full pattern. The adaptive resonance algorithm compares input and output patterns using an AND operation and vigilance threshold to determine if the weight adjustments should be made or if a new neuron is needed to represent the input.
This document outlines a course on neural networks and fuzzy systems. The course is divided into two parts, with part one focusing on neural networks over 11 weeks, covering topics like perceptrons, multi-layer feedforward networks, and unsupervised learning. Part two focuses on fuzzy systems over 4 weeks, covering fuzzy set theory and fuzzy systems. The document also provides details on concepts like linear separability, decision boundaries, perceptron learning algorithms, and using neural networks to solve problems like AND, OR, and XOR gates.
This document provides an overview of support vector machines (SVMs) for machine learning. It explains that SVMs find the optimal separating hyperplane that maximizes the margin between examples of separate classes. This is achieved by formulating SVM training as a convex optimization problem that can be solved efficiently. The document discusses how SVMs can handle non-linear decision boundaries using the "kernel trick" to implicitly map examples to higher-dimensional feature spaces without explicitly performing the mapping.
The document provides an overview of artificial neural networks (ANNs) and the perceptron learning algorithm. It discusses how biological neurons inspire ANNs and how a basic perceptron works using a simple example with inputs, weights, and outputs. The perceptron learning algorithm is then explained, which updates weights based on whether the perceptron's prediction was correct or incorrect on each training example. Finally, the document introduces multilayer perceptrons which can solve non-linearly separable problems by connecting multiple perceptron layers together through a process called backpropagation.
Data science involves extracting insights from large volumes of data. It is an interdisciplinary field that uses techniques from statistics, machine learning, and other domains. The document provides examples of classification algorithms like k-nearest neighbors, naive Bayes, and perceptrons that are commonly used in data science to build models for tasks like spam filtering or sentiment analysis. It also discusses clustering, frequent pattern mining, and other machine learning concepts.
This document provides an overview of single layer perceptrons and their use in classification tasks. It discusses the McCulloch-Pitts neuron model and how networks of these neurons can be connected to implement logic gates. It then introduces the perceptron as a single layer feedforward network and describes how to train perceptrons using supervised learning and the perceptron learning rule to classify data. Finally, it provides an example classification task of distinguishing trucks using their mass and length attributes.
Machine Learning: The Bare Math Behind LibrariesJ On The Beach
During this presentation, we will answer how much you’ll need to invest in a superhero costume to be as popular as Superman. We will generate a unique logo which will stand against the ever popular Batman and create new superhero teams. We shall achieve it using linear regression and neural networks.
Machine learning is one of the hottest buzzwords in technology today as well as one of the most innovative fields in computer science – yet people use libraries as black boxes without basic knowledge of the field. In this session, we will strip them to bare math, so next time you use a machine learning library, you’ll have a deeper understanding of what lies underneath.
During this session, we will first provide a short history of machine learning and an overview of two basic teaching techniques: supervised and unsupervised learning.
We will start by defining what machine learning is and equip you with an intuition of how it works. We will then explain the gradient descent algorithm with the use of simple linear regression to give you an even deeper understanding of this learning method. Then we will project it to supervised neural networks training.
Within unsupervised learning, you will become familiar with Hebb’s learning and learning with concurrency (winner takes all and winner takes most algorithms). We will use Octave for examples in this session; however, you can use your favourite technology to implement presented ideas.
Our aim is to show the mathematical basics of neural networks for those who want to start using machine learning in their day-to-day work or use it already but find it difficult to understand the underlying processes. After viewing our presentation, you should find it easier to select parameters for your networks and feel more confident in your selection of network type, as well as be encouraged to dive into more complex and powerful deep learning methods.
This document provides an introduction to support vector machines (SVMs) for text classification. It discusses how SVMs find an optimal separating hyperplane that maximizes the margin between classes. SVMs can handle non-linear classification through the use of kernels, which map data into a higher-dimensional feature space. The document outlines the mathematical formulations of linear and soft-margin SVMs, explains how the kernel trick allows evaluating inner products implicitly in that feature space, and summarizes how SVMs are used for classification tasks.
This document provides an overview of optimization techniques. It defines optimization as identifying variable values that minimize or maximize an objective function subject to constraints. It then discusses various applications of optimization in finance, engineering, and data modeling. The document outlines different types of optimization problems and algorithms. It provides examples of unconstrained optimization algorithms like gradient descent, conjugate gradient, Newton's method, and BFGS. It also discusses the Nelder-Mead simplex algorithm for constrained optimization and compares the performance of these algorithms on sample problems.
This document provides an overview of a single variable calculus course taught by Dr. Abdulla Al-Othman. The course covers differentiation and integration, including key ideas, theory, proofs of rules and theorems, and examples of applying differentiation and integration to polynomials, trigonometric, exponential, and logarithmic functions. The document also briefly introduces differential equations and techniques for solving first order ordinary differential equations.
Two algorithms to accelerate training of back-propagation neural networksESCOM
This document proposes two algorithms to initialize the weights of neural networks to accelerate training. Algorithm I performs a step-by-step orthogonalization of the input matrices to drive them towards a diagonal form, aiming to place the network closer to the convergence points of the activation function. Algorithm II aims to jointly diagonalize the input matrices to also drive them towards a diagonal form. The algorithms are shown to significantly reduce training time compared to random initialization, though Algorithm I works best when the activation function has φ(0)>1.
The document discusses various greedy algorithms including knapsack problems, minimum spanning trees, shortest path algorithms, and job sequencing. It provides descriptions of greedy algorithms, examples to illustrate how they work, and pseudocode for algorithms like fractional knapsack, Prim's, Kruskal's, Dijkstra's, and job sequencing. Key aspects covered include choosing the best option at each step and building up an optimal solution incrementally using greedy choices.
Self-organizing networks can perform unsupervised clustering by mapping high-dimensional input patterns into a smaller number of clusters in output space through competitive learning. Fixed weight competitive networks like Maxnet, Mexican Hat net, and Hamming net use competitive learning with fixed weights. Maxnet uses winner-take-all competition to select the neuron whose weights best match the input. Mexican Hat net has both excitatory and inhibitory connections between neurons to enhance contrast. Hamming net determines which exemplar vector most closely matches the input using the Hamming distance measure.
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.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
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.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
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%.
4. CS 472 - Perceptron 4
Perceptron Learning Algorithm
First neural network learning model in the 1960’s
Simple and limited (single layer model)
Basic concepts are similar for multi-layer models so this is
a good learning tool
Still used in some current applications (large business
problems, where intelligibility is needed, etc.)
5. CS 472 - Perceptron 5
Perceptron Node – Threshold Logic Unit
x1
xn
x2
w1
w2
wn
z
q
q
q
<
=
³
å
å
=
=
i
n
i
i
i
n
i
i
w
x
z
w
x
1
1
if
0
if
1
𝜃
6. CS 472 - Perceptron 6
Perceptron Node – Threshold Logic Unit
x1
xn
x2
w1
w2
wn
z
q
q
<
=
³
å
å
=
=
i
n
i
i
i
n
i
i
w
x
z
w
x
1
1
if
0
if
1
• Learn weights such that an objective
function is maximized.
• What objective function should we use?
• What learning algorithm should we use?
𝜃
7. CS 472 - Perceptron 7
Perceptron Learning Algorithm
x1
x2
z
q
q
<
=
³
å
å
=
=
i
n
i
i
i
n
i
i
w
x
z
w
x
1
1
if
0
if
1
.4
-.2
.1
x1 x2 t
0
1
.1
.3
.4
.8
8. CS 472 - Perceptron 8
First Training Instance
.8
.3
z
q
q
<
=
³
å
å
=
=
i
n
i
i
i
n
i
i
w
x
z
w
x
1
1
if
0
if
1
.4
-.2
.1
net = .8*.4 + .3*-.2 = .26
=1
x1 x2 t
0
1
.1
.3
.4
.8
9. CS 472 - Perceptron 9
Second Training Instance
.4
.1
z
q
q
<
=
³
å
å
=
=
i
n
i
i
i
n
i
i
w
x
z
w
x
1
1
if
0
if
1
.4
-.2
.1
x1 x2 t
0
1
.1
.3
.4
.8
net = .4*.4 + .1*-.2 = .14
=1
Dwi = (t - z) * c * xi
10. CS 472 - Perceptron 10
Perceptron Rule Learning
Dwi = c(t – z) xi
Where wi is the weight from input i to perceptron node, c is the learning
rate, t is the target for the current instance, z is the current output, and xi is
ith input
Least perturbation principle
– Only change weights if there is an error
– small c rather than changing weights sufficient to make current pattern correct
– Scale by xi
Create a perceptron node with n inputs
Iteratively apply a pattern from the training set and apply the perceptron
rule
Each iteration through the training set is an epoch
Continue training until total training set error ceases to improve
Perceptron Convergence Theorem: Guaranteed to find a solution in finite
time if a solution exists
12. CS 472 - Perceptron 12
Augmented Pattern Vectors
1 0 1 -> 0
1 0 0 -> 1
Augmented Version
1 0 1 1 -> 0
1 0 0 1 -> 1
Treat threshold like any other weight. No special case.
Call it a bias since it biases the output up or down.
Since we start with random weights anyways, can ignore
the - notion, and just think of the bias as an extra
available weight. (note the author uses a -1 input)
Always use a bias weight
13. CS 472 - Perceptron 13
Perceptron Rule Example
Assume a 3 input perceptron plus bias (it outputs 1 if net > 0, else 0)
Assume a learning rate c of 1 and initial weights all 0: Dwi = c(t – z) xi
Training set 0 0 1 -> 0
1 1 1 -> 1
1 0 1 -> 1
0 1 1 -> 0
Pattern Target (t) Weight Vector (wi) Net Output (z) DW
0 0 1 1 0 0 0 0 0
14. CS 472 - Perceptron 14
Example
Assume a 3 input perceptron plus bias (it outputs 1 if net > 0, else 0)
Assume a learning rate c of 1 and initial weights all 0: Dwi = c(t – z) xi
Training set 0 0 1 -> 0
1 1 1 -> 1
1 0 1 -> 1
0 1 1 -> 0
Pattern Target (t) Weight Vector (wi) Net Output (z) DW
0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 0 0 0 0
15. CS 472 - Perceptron 15
Example
Assume a 3 input perceptron plus bias (it outputs 1 if net > 0, else 0)
Assume a learning rate c of 1 and initial weights all 0: Dwi = c(t – z) xi
Training set 0 0 1 -> 0
1 1 1 -> 1
1 0 1 -> 1
0 1 1 -> 0
Pattern Target (t) Weight Vector (wi) Net Output (z) DW
0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 0 0 0 0 0 0 1 1 1 1
1 0 1 1 1 1 1 1 1
16. Peer Instruction
I pose a challenge question (often multiple choice), which
will help solidify understanding of topics we have studied
– Might not just be one correct answer
You each get some time (1-2 minutes) to come up with
your answer and vote – use Mentimeter (anonymous)
Then you get some time to convince your group
(neighbors) why you think you are right (2-3 minutes)
– Learn from and teach each other!
You vote again. May change your vote if you want.
We discuss together the different responses, show the
votes, give you opportunity to justify your thinking, and
give you further insights
CS 472 - Perceptron 16
17. Peer Instruction (PI) Why
Studies show this approach improves learning
Learn by doing, discussing, and teaching each other
– Curse of knowledge/expert blind-spot
– Compared to talking with a peer who just figured it out and who can
explain it in your own jargon
– You never really know something until you can teach it to someone
else – More improved learning!
Learn to reason about your thinking and answers
More enjoyable - You are involved and active in the learning
CS 472 - Perceptron 17
18. How Groups Interact
Best if group members have different initial answers
3 is the “magic” group number
– You can self-organize "on-the-fly" or sit together specifically to be a
group
– Can go 2-4 on a given day to make sure everyone is involved
Teach and learn from each other: Discuss, reason, articulate
If you know the answer, listen to where colleagues are coming
from first, then be a great humble teacher, you will also learn by
doing that, and you’ll be on the other side in the future
– I can’t do that as well because every small group has different
misunderstandings and you get to focus on your particular questions
Be ready to justify to the class your vote and justifications!
CS 472 - Perceptron 18
19. CS 472 - Perceptron 19
**Challenge Question** - Perceptron
Assume a 3 input perceptron plus bias (it outputs 1 if net > 0, else 0)
Assume a learning rate c of 1 and initial weights all 0: Dwi = c(t – z) xi
Training set 0 0 1 -> 0
1 1 1 -> 1
1 0 1 -> 1
0 1 1 -> 0
Pattern Target (t) Weight Vector (wi) Net Output (z) DW
0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 0 0 0 0 0 0 1 1 1 1
1 0 1 1 1 1 1 1 1
Once it converges the final weight vector will be
A. 1 1 1 1
B. -1 0 1 0
C. 0 0 0 0
D. 1 0 0 0
E. None of the above
20. CS 472 - Perceptron 20
Example
Assume a 3 input perceptron plus bias (it outputs 1 if net > 0, else 0)
Assume a learning rate c of 1 and initial weights all 0: Dwi = c(t – z) xi
Training set 0 0 1 -> 0
1 1 1 -> 1
1 0 1 -> 1
0 1 1 -> 0
Pattern Target (t) Weight Vector (wi) Net Output (z) DW
0 0 1 1 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 0 0 0 0 0 0 1 1 1 1
1 0 1 1 1 1 1 1 1 3 1 0 0 0 0
0 1 1 1 0 1 1 1 1
23. CS 472 - Perceptron 23
Perceptron Homework
Assume a 3 input perceptron plus bias (it outputs 1 if net > 0, else 0)
Assume a learning rate c of 1 and initial weights all 1: Dwi = c(t – z) xi
Show weights after each pattern for just one epoch
Training set 1 0 1 -> 0
1 .5 0 -> 0
1 -.4 1 -> 1
0 1 .5 -> 1
Pattern Target (t) Weight Vector (wi) Net Output (z) DW
1 1 1 1
24. CS 472 - Perceptron 24
Training Sets and Noise
Assume a Probability of Error at each input and output
value each time a pattern is trained on
0 0 1 0 1 1 0 0 1 1 0 -> 0 1 1 0
i.e. P(error) = .05
Or a probability that the algorithm is applied wrong
(opposite) occasionally
Averages out over learning
26. CS 472 - Perceptron 26
If no bias weight, the
hyperplane must go
through the origin.
Note that since 𝛳 = -bias,
the equation with bias is:
X2 = (-W1/W2)X1 - bias/W2
28. CS 472 - Perceptron 28
Linear Separability and Generalization
When is data noise vs. a legitimate exception
29. CS 472 - Perceptron 29
Limited Functionality of Hyperplane
30. How to Handle Multi-Class Output
This is an issue with learning models which only support binary
classification (perceptron, SVM, etc.)
Create 1 perceptron for each output class, where the training set
considers all other classes to be negative examples (one vs the
rest)
– Run all perceptrons on novel data and set the output to the class of the
perceptron which outputs high
– If there is a tie, choose the perceptron with the highest net value
Another approach: Create 1 perceptron for each pair of output
classes, where the training set only contains examples from the
2 classes (one vs one)
– Run all perceptrons on novel data and set the output to be the class
with the most wins (votes) from the perceptrons
– In case of a tie, use the net values to decide
– Number of models grows by the square of the output classes
CS 472 - Perceptron 30
32. Objective Functions: Accuracy/Error
How do we judge the quality of a particular model (e.g.
Perceptron with a particular setting of weights)
Consider how accurate the model is on the data set
– Classification accuracy = # Correct/Total instances
– Classification error = # Misclassified/Total instances (= 1 – acc)
Usually minimize a Loss function (aka cost, error)
For real valued outputs and/or targets
– Pattern error = Target – output: Errors could cancel each other
S|tj – zj| (L1 loss), where j indexes all outputs in the pattern
Common approach is Squared Error = S(tj – zj)2 (L2 loss)
– Total sum squared error = S pattern squared errors = S S (tij – zij)2
where i indexes all the patterns in training set
For nominal data, pattern error is typically 1 for a mismatch and
0 for a match
– For nominal (including binary) output and targets, SSE and
classification error are equivalent
CS 472 - Perceptron 32
33. Mean Squared Error
Mean Squared Error (MSE) – SSE/n where n is the number of
instances in the data set
– This can be nice because it normalizes the error for data sets of
different sizes
– MSE is the average squared error per pattern
Root Mean Squared Error (RMSE) – is the square root of the
MSE
– This puts the error value back into the same units as the features and
can thus be more intuitive
Since we squared the error on the SSE
– RMSE is the average distance (error) of targets from the outputs in the
same scale as the features
– Note RMSE is the root of the total data set MSE, and NOT the sum of
the root of each individual pattern MSE
CS 472 - Perceptron 33
34. **Challenge Question** - Error
Given the following data set, what is the L1 (S|ti – zi|), SSE (L2)
(S(ti – zi)2), MSE, and RMSE error for the entire data set?
CS 472 - Perceptron 34
x y Output Target Data Set
2 -3 1 1
0 1 0 1
.5 .6 .8 .2
L1 x
SSE x
MSE x
RMSE x
A. .4 1 1 1
B. 1.6 2.36 1 1
C. .4 .64 .21 0.453
D. 1.6 1.36 .67 .82
E. None of the above
35. **Challenge Question** - Error
CS 472 - Perceptron 35
x y Output Target Data Set
2 -3 1 1
0 1 0 1
.5 .6 .8 .2
L1 1.6
SSE 1.36
MSE 1.36/3 = .453
RMSE .45^.5 = .67
A. .4 1 1 1
B. 1.6 2.36 1 1
C. .4 .64 .21 0.453
D. 1.6 1.36 .67 .82
E. None of the above
Given the following data set, what is the L1 (S|ti – zi|), SSE (L2)
(S(ti – zi)2), MSE, and RMSE error for the entire data set?
36. SSE Homework
Given the following data set, what is the L1, SSE (L2),
MSE, and RMSE error of Output1, Output2, and the entire
data set? Fill in cells that have an x.
CS 472 - Perceptron 36
x y Output1 Target1 Output2 Target 2 Data Set
-1 -1 0 1 .6 1.0
-1 1 1 1 -.3 0
1 -1 1 0 1.2 .5
1 1 0 0 0 -.2
L1 x x x
SSE x x x
MSE x x x
RMSE x x x
37. CS 472 - Perceptron 37
Gradient Descent Learning: Minimize
(Maximze) the Objective Function
Total SSE:
Sum
Squared
Error
S (t – z)2
0
Error Landscape
Weight Values
38. CS 472 - Perceptron 38
Goal is to decrease overall error (or other loss function)
each time a weight is changed
Total Sum Squared error one possible loss function
E: S (t – z)2
Seek a weight changing algorithm such that is
negative
If a formula can be found then we have a gradient descent
learning algorithm
Delta rule is a variant of the perceptron rule which gives a
gradient descent learning algorithm with perceptron nodes
Deriving a Gradient Descent Learning
Algorithm
ij
w
E
¶
¶
39. CS 472 - Perceptron 39
Delta rule algorithm
Delta rule uses (target - net) before the net value goes through the
threshold in the learning rule to decide weight update
Weights are updated even when the output would be correct
Because this model is single layer and because of the SSE objective
function, the error surface is guaranteed to be parabolic with only one
minima
Learning rate
– If learning rate is too large can jump around global minimum
– If too small, will get to minimum, but will take a longer time
– Can decrease learning rate over time to give higher speed and still
attain the global minimum (although exact minimum is still just for
training set and thus…)
Dwi = c(t -net)xi
40. Batch vs Stochastic Update
To get the true gradient with the delta rule, we need to sum
errors over the entire training set and only update weights
at the end of each epoch
Batch (gradient) vs stochastic (on-line, incremental)
– SGD (Stochastic Gradient Descent)
– With the stochastic delta rule algorithm, you update after every pattern,
just like with the perceptron algorithm (even though that means each
change may not be along the true gradient)
– Stochastic is more efficient and best to use in almost all cases, though not
all have figured it out yet
– We’ll talk about this in more detail when we get to Backpropagation
CS 472 - Perceptron 40
41. CS 472 - Perceptron 41
Perceptron rule vs Delta rule
Perceptron rule (target - thresholded output) guaranteed to
converge to a separating hyperplane if the problem is
linearly separable. Otherwise may not converge – could
get in a cycle
Singe layer Delta rule guaranteed to have only one global
minimum. Thus, it will converge to the best SSE solution
whether the problem is linearly separable or not.
– Could have a higher misclassification rate than with the perceptron
rule and a less intuitive decision surface – we will discuss this later
with regression
Stopping Criteria – For these models we stop when no
longer making progress
– When you have gone a few epochs with no significant
improvement/change between epochs (including oscillations)
42. CS 472 - Perceptron 42
Exclusive Or
X1
X2
1
1
0
0
Is there a dividing hyperplane?
43. CS 472 - Perceptron 43
Linearly Separable Boolean Functions
d = # of dimensions (i.e. inputs)
44. CS 472 - Perceptron 44
Linearly Separable Boolean Functions
d = # of dimensions
P = 2d = # of Patterns
45. CS 472 - Perceptron 45
Linearly Separable Boolean Functions
d = # of dimensions
P = 2d = # of Patterns
2P = 22d
= # of Functions
n Total Functions Linearly Separable Functions
0 2 2
1 4 4
2 16 14
47. CS 472 - Perceptron 47
Linearly Separable Functions
LS(P,d) = 2 å
i=0
d
(P-1)!
(P-1-i)!i! for P > d
= 2P for P ≤ d
(All patterns for d=P)
i.e. all 8 ways of dividing 3 vertices of a
cube for d=P=3
Where P is the # of patterns for training and
d is the # of inputs
lim
d -> ∞ (# of LS functions) = ∞
48. Linear Models which are Non-Linear in the
Input Space
So far we have used
We could preprocess the inputs in a non-linear way and do
To the perceptron algorithm it is the same but with
more/different inputs. It still uses the same learning algorithm.
For example, for a problem with two inputs x and y (plus the
bias), we could also add the inputs x2, y2, and x·y
The perceptron would just think it is a 5-dimensional task, and it
is linear (5-d hyperplane) in those 5 dimensions
– But what kind of decision surfaces would it allow for the original 2-d
input space?
CS 472 - Perceptron 48
f (x,w) = sign( wixi
1=1
n
å )
f (x,w) = sign( wifi(x
1=1
m
å ))
49. Quadric Machine
All quadratic surfaces (2nd order)
– ellipsoid
– parabola
– etc.
That significantly increases the number of problems that
can be solved
But still many problem which are not quadrically separable
Could go to 3rd and higher order features, but number of
possible features grows exponentially
Multi-layer neural networks will allow us to discover high-
order features automatically from the input space
CS 472 - Perceptron 49
50. Simple Quadric Example
What is the decision surface for a 1-d (1 input) problem?
Perceptron with just feature f1 cannot separate the data
Could we add a transformed feature to our perceptron?
CS 472 - Perceptron 50
-3 -2 -1 0 1 2 3
f1
51. Simple Quadric Example
Perceptron with just feature f1 cannot separate the data
Could we add a transformed feature to our perceptron?
f2 = f1
2
CS 472 - Perceptron 51
-3 -2 -1 0 1 2 3
f1
52. Simple Quadric Example
Perceptron with just feature f1 cannot separate the data
Could we add another feature to our perceptron f2 = f1
2
Note could also think of this as just using feature f1 but now
allowing a quadric surface to divide the data
– Note that f1 not actually needed in this case
CS 472 - Perceptron 52
-3 -2 -1 0 1 2 3
f1
-3 -2 -1 0 1 2 3
f2
f1
53. Quadric Machine Homework
Assume a 2-input perceptron expanded to be a quadric (2nd order)
perceptron, with 5 input weights (x, y, x·y, x2, y2) and the bias weight
– Assume it outputs 1 if net > 0, else 0
Assume a learning rate c of .5 and initial weights all 0
– Dwi = c(t – z) xi
Show all weights after each pattern for one epoch with the following
training set
CS 472 - Perceptron 53
x y Target
0 .4 0
-.1 1.2 1
.5 .8 0