In this Machine Learning tutorial, we will cover the top Neural Network Algorithms. These algorithms are used to train the Artificial Neural Network. This blog provides you a deep learning of the Gradient Descent, Evolutionary Algorithms, and Genetic Algorithm in Neural Network.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
A Review on Feature Selection Methods For Classification TasksEditor IJCATR
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Introduction to Optimization with Genetic Algorithm (GA)Ahmed Gad
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
References:
Eiben, Agoston E., and James E. Smith. Introduction to evolutionary computing. Vol. 53. Heidelberg: springer, 2003.
https://www.linkedin.com/pulse/introduction-optimization-genetic-algorithm-ahmed-gad
https://www.kdnuggets.com/2018/03/introduction-optimization-with-genetic-algorithm.html
A Review on Feature Selection Methods For Classification TasksEditor IJCATR
In recent years, application of feature selection methods in medical datasets has greatly increased. The challenging task in
feature selection is how to obtain an optimal subset of relevant and non redundant features which will give an optimal solution without
increasing the complexity of the modeling task. Thus, there is a need to make practitioners aware of feature selection methods that have
been successfully applied in medical data sets and highlight future trends in this area. The findings indicate that most existing feature
selection methods depend on univariate ranking that does not take into account interactions between variables, overlook stability of the
selection algorithms and the methods that produce good accuracy employ more number of features. However, developing a universal
method that achieves the best classification accuracy with fewer features is still an open research area.
Guest Lecture about genetic algorithms in the course ECE657: Computational Intelligence/Intelligent Systems Design, Spring 2016, Electrical and Computer Engineering (ECE) Department, University of Waterloo, Canada.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
PERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTINGIJMIT JOURNAL
This paper presents performance analysis of hybrid model comprise of concordance and Genetic
Programming (GP) to forecast financial market with some existing models. This scheme can be used for in
depth analysis of stock market. Different measures of concordances such as Kendall’s Tau, Gini’s Mean
Difference, Spearman’s Rho, and weak interpretation of concordance are used to search for the pattern in
past that look similar to present. Genetic Programming is then used to match the past trend to present
trend as close as possible. Then Genetic Program estimates what will happen next based on what had
happened next. The concept is validated using financial time series data (S&P 500 and NASDAQ indices)
as sample data sets. The forecasted result is then compared with standard ARIMA model and other model
to analyse its performance
Approaches to gather business requirements, defining problem statements, business requirements for
use case development, Assets for development of IoT solutions
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
PERFORMANCE ANALYSIS OF HYBRID FORECASTING MODEL IN STOCK MARKET FORECASTINGIJMIT JOURNAL
This paper presents performance analysis of hybrid model comprise of concordance and Genetic
Programming (GP) to forecast financial market with some existing models. This scheme can be used for in
depth analysis of stock market. Different measures of concordances such as Kendall’s Tau, Gini’s Mean
Difference, Spearman’s Rho, and weak interpretation of concordance are used to search for the pattern in
past that look similar to present. Genetic Programming is then used to match the past trend to present
trend as close as possible. Then Genetic Program estimates what will happen next based on what had
happened next. The concept is validated using financial time series data (S&P 500 and NASDAQ indices)
as sample data sets. The forecasted result is then compared with standard ARIMA model and other model
to analyse its performance
Approaches to gather business requirements, defining problem statements, business requirements for
use case development, Assets for development of IoT solutions
Prediction of Euro 50 Using Back Propagation Neural Network (BPNN) and Geneti...AI Publications
Modeling time series is often associated with the process forecasts certain characteristics in the next period. One of the methods forecasts that developed nowadays is using artificial neural network or more popularly known as a neural network. Use neural network in forecasts time series can be a good solution, but the problem is network architecture and the training method in the right direction. One of the choices that might be using a genetic algorithm. A genetic algorithm is a search algorithm stochastic resonance based on how it works by the mechanisms of natural selection and genetic variation that aims to find a solution to a problem. This algorithm can be used as teaching methods in train models are sent back propagation neural network. The application genetic algorithm and neural network for divination time series aim to get the weight optimum. From the training and testing on the data index share price euro 50 obtained by the RMSE testing 27.8744 and 39.2852 RMSE training. The weight or parameters that produced by has reached an optimum level in second-generation 1000 with the best fitness and the average 0.027771 the fitness of 0.0027847.Model is good to be used to give a prediction that is quite accurate information that is shown by the close target with the output.
Artificial Intelligence in Robot Path Planningiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
How Does XfilesPro Ensure Security While Sharing Documents in Salesforce?XfilesPro
Worried about document security while sharing them in Salesforce? Fret no more! Here are the top-notch security standards XfilesPro upholds to ensure strong security for your Salesforce documents while sharing with internal or external people.
To learn more, read the blog: https://www.xfilespro.com/how-does-xfilespro-make-document-sharing-secure-and-seamless-in-salesforce/
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
2. 1. Objective
In this Machine Learning tutorial, we will cover the top Neural Network
Algorithms. These algorithms are used to train the Artificial Neural
Network. This blog provides you a deep learning of the Gradient
Descent, Evolutionary Algorithms, and Genetic Algorithm in Neural
Network.
2. Top Neural Network Algorithms
Learning of neural network takes place on the basis of a sample of the
population under study. During the course of learning, compare the value
delivered by output unit with actual value. After that adjust the weights of
all units so to improve the prediction.
There are many Neural Network Algorithms are available for
training Artificial Neural Network. Let us now see some important
Algorithms for training Neural Networks:
3. • Gradient Descent – Used to find the local minimum of a function.
• Evolutionary Algorithms – Based on the concept of natural selection
or survival of the fittest in Biology.
• Genetic Algorithm – Enable the most appropriate rules for the
solution of a problem and select it. So, that they send their ‘genetic
material’ to ‘child’ rules. We will learn about them in details below.
Get the introduction of learning rules in Neural Network for more
understanding of Neural Network Algorithms.
2.1. Gradient Descent
We use the gradient descent algorithm to find the local smallest of a
function. This algorithm converges to the local smallest. By approaching
proportional to the negative of the gradient of the function. To find local
maxima, take the steps proportional to the positive gradient of the
function. This is gradient ascendant process.
In linear models, error surface is well defined and well known
mathematical object in shape of a parabola. Then find the least point by
calculation. Unlike linear models, neural networks are complex nonlinear
models. Here, the error surface has an irregular layout, crisscrossed with
hills, valleys, plateau, and deep ravines. To find the least point on this
surface, for which no maps are available, the user must explore it.
In this algorithm, you move over the error surface by following the line
with the greatest slope. It also offers the possibility of reaching the lowest
possible point. You then have to work out at the optimal rate at which you
should travel down the slope.
The correct speed is proportional to the slope of the surface and the
learning rate. Learning rate controls the extent of modification of the
weights during the learning process.
Hence, the moment of a neural network can affect the performance
of multilayer perceptron.
2.2. Evolutionary Algorithms
This algorithm based on the concept of natural selection or survival of the
fittest in Biology. Concept of natural selection states that – for a given
4. population, environment conditions use a pressure that results in the
rise of the fittest in that population.
To measure fittest in a given population, you can apply a function as an
abstract measure.
In the context of evolutionary algorithms, refer recombination to as an
operator. Then apply it to two or more candidates known as parents, and
result in one of more new candidates known as children. Apply the
mutation on a single candidate and results in a new candidate. By applying
recombination and mutation, we can get a set of new candidates to place in
the next generation based on their fittest measure.
The two basic elements of evolutionary algorithms are:
• Variation operators (recombination and mutation)
• Selection process (selection of the fittest)
The common features of evolutionary algorithms are:
• Evolutionary algorithms are population based.
• Evolutionary algorithms use recombination mix candidates of a
population and create new candidates.
• On random selection evolutionary algorithm based.
Hence, on the basis of details and applied problems, we use various
formats of evolutionary algorithms.
Some common evolutionary algorithms are:
• Genetic Algorithm Genetic Algorithm – It provides the solution for
optimization problems. It provides the solution by the help of
natural evolution processes. Like mutation, recombination,
crossover, and inheritance.
• Genetic Programming – The genetic programming provides a
solution in the form of computer programs. By the ability to solve
computational problems accuracy of a program measures.
• Evolutionary Programming – In a simulated environment to develop
the AI we use it.
• Evolution Strategy It is an optimization algorithm. Grounded on the
concepts of the adaptation and the evolution in biological science.
5. • Neuroevolution – To train neural networks we uses
Neuroevolution. By specifying structure and connection weights
genomes uses to develop neural networks.
In all these algorithms, genetic algorithm is the most common evolutionary
algorithm.
2.3. Genetic Algorithm
Genetic algorithms, developed by John Holland’s group from the early
1970s. It enables the most appropriate rules for the solution of a problem
to be selected. So that they send their ‘genetic material’ (their variables
and categories) to ‘child’ rules.
Here refer a as a set of categories of variables. For example, customers
aged between 36 and 50, having financial assets of less than $20,000 and a
monthly income of more than $2000.
A rule is the equal of a branch of a decision tree; it is also analogous to a
gene. You can understand genes as units inside cells that control how
living organisms inherit features of their parents. Thus, Genetic algorithms
aim to reproduce the mechanisms of natural selection. By selecting the
rules best adapted to prediction and by crossing and mutating them until
get a predictive model.
Together with neural networks, they form the second type of algorithm.
Which mimics natural mechanisms to explain phenomena that are not
necassary natural.
The steps for executing genetic algorithms are:
• Step 1: Random generation of initial rules – Generate the rules first
with the constraint being that they must be all distinct. Each rule
contains a random number of variables chosen by user.
• Step 2: Selection of the best rules – Check the Rules in view of the
aim by fitness function to guide the evolution toward the best rules.
Best rules maximize the fitness function and retain with probability
that increases as the rule improves. Some rules will disappear while
others select several times.
6. • Step 3: Generation of new rules by mutation or crossing – First, go
to step 2 until the execution of the algorithm stops. Chosen rules
are randomly mutated or crossed. Mutation is replacement of a
variable or a category of original rule with another.
Crossing of 2 rules is exchange of some of their variables or categories to
produce 2 new rules. Crossing is more common than mutation.
Algorithm ends when 1 of the following 2 conditions meets:
• Specified number of iterations that reached.
• Starting from generation of rank n, rules of generations n, n-1 and
n-2 are (almost) identical.
3. Conclusion
In Conclusion, Artificial Neural Network is typically difficult to configure
and slow to train, but once prepared are very fast in the application. They
are generally designed as models to overcome the mathematical,
computational, and engineering problems. Since, there is a lot of research
in mathematics, neurobiology and computer science.
If you’d like to share your opinion and have any query about Artificial
Neural Network Algorithms, please do so in the comment section.