Neural Networks: What is it? How is it useful?

Innovation is crucial for collaborative environments. Quick history lesson: It’s estimated that people have only been around for 0.007% of the time the world has existed. Considering this and looking around at the changes we’ve seen (most of which are thanks to technology) what resources do you expect to have at your disposal in the next decade? 

In this world of technology, innovations that support data processing and analysis are highly sought after. Companies are finding ways to include these innovations in their software infrastructure to become more lucrative and scale for long-term success. To aid this demand for businesses, one of the hottest trends we’re seeing is that of neural networks.

What is a Neural Network?

This is a big term that many may be unfamiliar with, so let’s cover the basics of the concept. A neural network was inspired by the structure and function of an actual human brain and is a machine-learning model designed to perform like one. It’s made up of layers of interconnected "neurons," which are used to process and transmit information.

Every neuron in the system receives input from the other neurons, which will then process that input using an activation function, then the output will be sent to other neurons or an “output layer”. If I haven’t lost you yet, hang in there. The connection between these neurons is represented by “weights”, which are adjusted during the training process to optimize the performance of the model. 

The kinds of tasks neural networks may be used for include:

• Image classification: In this task, a neural network is used to recognize and classify objects in images. For example, different animals in a picture.

• Language translation: A neural network can be trained to translate text from one language to another. 

• Forecasting: Neural networks can be used to make predictions about future events based on data from the past. For example, a neural network might be trained to predict patterns in the stock market or the weather.

• Speech recognition

• Natural language processing: Neural networks can be used to analyze and understand various languages.

These networks can learn and improve their performance over time by adjusting the weights of the connections between the neurons based on the inputted data. Are you seeing the connection to how a person's mind works?

How do Businesses Benefit From Them?

So far we’ve covered this technology using complicated broad terms and concepts which would make the average business owner wonder how this can be used to their benefit. The top 5 industries that benefit most from neural networks include:

• Healthcare

• eCommerce

• Security

• Finance

• Logistics 

Companies of all sizes and in various industries (not limited to those listed above) continuously find productive uses for neural networks. These uses will vary from industry to industry but here are 3 common thread benefits:

1. Decision-making: Neural networks can analyze a lot of data and identify patterns/trends that may not be immediately obvious to people. This helps businesses lean towards more informed and accurate decisions.

2. Efficiency: Neural networks can automate tasks and processes that would otherwise require the attention of experts. The system thus increases the efficiency and productivity of the business’ processes

3. Security: Neural networks can be used to identify and prevent fraud or other security threats by analyzing patterns and anomalies in data.

The main thing to glean from these points is that the way a neural network benefits a business is going to be subjective. Every company/industry has its unique approaches and processes that require various types of software to handle the job. What matters is that these aspects are identified and outlined before any implementation process begins.

The Takeaway

Neural networks can learn and improve their performance over time and offer many benefits to businesses that incorporate them. Software, as always, is going to involve analysis when developing a plan for how it can support a business. This is why consulting with software experts is highly recommended before starting your quest. Remember, experience either redirects or enhances instinct, never does it fail it.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

7 Data Analytics Areas

Data analytics aids businesses to convert their raw business data into actionable insights. This environment is constantly changing, hence it is important for software companies to keep up and ensure businesses have the best services.

1. Financial Analytics

• Monitoring revenue, expenses, and profitability of a company.

• Profitability analysis and financial performance management.

• Budget planning, formulating long-term business plans.

• Financial risk forecasting and management.

  2. Customer Analytics

• Customer behavior analysis and predictive modeling.

• Customer segmentation for tailored marketing campaigns.

• Personalized cross-selling and upselling offers for extended customer lifetime value.

• Predicting customer attrition and customer churn risk management.

• Customer sentiment analysis for increasing product/service quality.

  3. Brand and Product Analytics

• Conducting product performance analysis.

• Tracking customer interactions with a product to identify pain points leading to churn.

• Conducting competitor benchmarking.

  4. Asset Analytics

• Real-time asset monitoring and tracking.

• Asset life cycle management.

• Predictive and preventive maintenance.

• Asset health prediction.

• Designing asset maintenance and replacement strategies.

  5. HR Analytics

• Employee/department performance monitoring and analysis.

• Employee experience and satisfaction analysis.

• Employee retention strategy optimization and management.

• Employee hiring strategy analysis and optimization.

  6. Supply Chain Analytics

• Identifying demand drivers, consumer demand forecasting, and planning.

• Supplier performance monitoring and evaluation.

• Predictive route optimization.

• Determining the optimal level of inventory to meet the demand and prevent stockouts, inventory planning, and management.

• Identifying patterns and trends throughout the supply chain for enhanced supply chain risks management.

  7. Manufacturing Analytics

• Overall equipment effectiveness analysis and optimization.

• Manufacturing process quality prediction and management.

• Equipment maintenance scheduling.

• Power consumption forecasting and optimization.

• Production loss root cause analysis.

Conclusion

There are many areas of analytics and as software evolves, more areas will continue to develop.

For more information on data analytics and software, visit ISUCorp.ca

GitHub vs GitLab: Which is Best for You?

An important aspect of the software development lifecycle is repository management. A Git repository is where you can collaborate, test, share, store web projects, and code. Since Git is distributed, you can have local repositories. This allows you to work on your code without having Internet access.

Using the right repository for your project is important for accelerating your software development initiative and efficiency. 

In this post, we’ll discuss two different Git repository managers - GitHub and GitLab to help you understand which fits your project best.

Let’s dive right in!

What is GitHub?

GitHub is a cloud-based repository management hosting service that provides a Web-based graphical interface. It serves as a hosting site where web developers (novice programmers and seasoned engineers) can work reciprocally, upload, and improve the code they create.

Additionally, it offers a robust version control system, which allows for consistent collaboration without jeopardizing the integrity of the original project. You can use GitHub for both public and private projects.

What is GitLab?

This is an open-source code repository, as well as a collaborative development platform. It offers a location for code storage and collaborative development of projects. Like GitHub, GitLab also offers version control that allows users to check previous code. GitLab supports both public and private development branches and offers features for bug tracking and project management. 

Similarities Between GitHub and GitLab

Since both are developed on the same Git basis of version control, their functions are similar. 

Third-party Integrations:

Both GitHub and GitLab offer a wide range of third-party integrations. Integrating your version control system with other applications enriches your workflow and can boost productivity for your developers and other employees connected to the software.

Tracking:

GitHub, as well as GitLab, offers a simple issue tracker that lets you change status and assign owners accordingly. Both of them have great reporting tools, including bug reporting and user feedback that can be accessed instantly.

Labels: 

Both utilize a simple system of labeling that allows you to assign informative titles to easily categorize issues, merge requests, and epics. 

Issues: 

Both GitHub and GitLab offer features like setting the issue status, assignees, milestones, and they can each be filtered without a challenging process.

Enterprise solutions: 

Both GitHub and GitLab offer enterprise solutions for businesses.

Differences Between GitHub and GitLab

Open Source: 

Both are open-source platforms and they both provide free private repositories for open-source projects. However, GitHub allows you to have unlimited collaborators and unlimited repositories, while GitLab allows an unlimited number of users with unlimited free private repositories. 

Authentication Level: 

With GitHub, you can decide who gets reading or writing access to your repositories. While in GitLab, users have different levels of access based on their roles. 

Import/Export Features:

GitHub is more restrictive when it comes to import and export features of existing GitHub repositories as it does not provide step-by-by documentation. On the contrary, GitHub provides a GitHub importer tool to make importing and exporting easy. 

GitLab offers quite extensive documents on how to import and export data from external sources including GitHub, Bitbucket, and any GIT URL. Also, GitLab allows you to export projects to other systems.

GitLab Vs GitHub Enterprise:

The decision to use either GitLab or GitHub is highly dependent on the project and organization.

GitLab enterprise is significantly cheaper compared to GitHub. If you are operating on a tight budget, GitLab is a preferred option. It also provides a feature-rich experience. 

Still, GitHub with its established strong market position is highly popular among larger development teams and organizations. 

GitHub Vs GitLab Performance:

GitHub focuses more on high availability and infrastructure performance, whilst delegating other functionalities to third-party tools. Meanwhile, GitLab puts more emphasis on providing maximum features in a robust platform for end-to-end development management.

GitLab CI vs GitHub Actions:

One of the differences between GitLab and GitHub is the built-in CI of GitLab. GitLab provides its CI for free. It has been addressing the DevOps market earlier than its competitor, as well as offering an operation dashboard that lets you understand the dependencies of your development and DevOps efforts. 

Additionally, GitHub released Actions in 2019. Actions allow you to write tasks that automate and customize the development workflow. But it does not come with a deployment platform and needs additional applications. 

Wrapping It Up

Both GitLab and GitHub are web-based repository managers that allow collaborating on code. GitLab has amazing and unique features that allow you to go from development to cloud without necessarily having to use other third-party tools. GitLab has lots of features. On the other hand, GitHub is trusted by many developers. It provides you with a larger number of integrations and offers collaboration tools.

GitHub Actions makes development faster and easier. But for the DevOps lifecycle, GitLab ranks better with its built-in CI/CD framework and monitoring features.

Your choice of repository management platform depends on the objectives you need to achieve and what suits your needs best.

Get in touch with us to discuss more.