What are Machine Learning VS Deep Learning?

Lire plus

What is the difference between deep learning vs machine learning? Deep learning constitutes a type of machine learning, which is a category within artificial intelligence or AI. Machine learning refers to the concept of computers being able to think and act with less human intervention. 

On the other hand, deep learning is the process of enabling computers to learn to think using structures modeled on the human brain. In fact, machine learning requires less computing power vs deep learning, and deep learning typically needs less ongoing human intervention. 

In other words, deep learning can analyze images, videos, and unstructured data in ways machine learning can’t easily do. Every industry will have career paths that involve machine learning and deep learning.

With new computing technology, machine learning today is different from machine learning in the past. The idea of machine learning originated from pattern recognition. It combines this concept with the concept that computers can learn patterns without being programmed to perform specific functions. 

Researchers interested in artificial intelligence or AI wanted to see if computers could learn from data and information. The iterative aspect of machine learning enables the independent adaptability of the model when it is exposed to new data. Computers can learn from previous calculations to achieve reliable, repeatable decisions and optimal results. 

Many machine learning algorithms have been used for a long time, but the ability to apply complicated mathematical calculations to vast amounts of data has recently been developed. A typical example of a machine learning application is the self-driving Google car.

Why is machine learning important? 

Emerging interest in machine learning is due to the same reasons that have made data mining and analysis more popular than ever. Concepts such as the growing volumes and varieties of accessible data, affordable data storage, and computational processing that is cheaper and more powerful are the reasons machine learning is gaining more importance. 

All of these elements combined mean it’s possible to quickly and automatically produce mechanisms that can analyze more complex and large data and deliver faster and more accurate results. By building precise models, an organization has a better chance of identifying profitable opportunities and avoiding unknown risks.

Creating a good machine learning system requires data preparation functions, simple and advanced algorithms, automation and iterative processes, scalability, and ensemble modeling.

Machine Learning VS Deep Learning Mechanisms

Machine Learning Mechanism

The machine learning mechanism can be broken down into three elements: the decision phase, the error function, and the optimization model.

The decision phase: this is the prediction phase. Machine learning uses input data to produce a pattern estimate about the data.

The error function: this function is valuable in the evaluation of the previously created pattern. It can also make a comparison to evaluate the correctness and preciseness of the model.

The optimization model: this phase comprises weight adjustments in order to reduce the discrepancy between the model estimate and the known example. Afterward, the algorithm updates the weight autonomously and optimizes the model until it is considered accurate and concise.

Deep Leaning Mechanism

Deep learning is an instance of machine learning and AI or artificial intelligence that mimics the way humans acquire specific knowledge. Deep learning is an essential component of data science, including statistics and predictive models. This is very useful for data scientists who need to gather, assess, interpret, and analyze vast amounts of data. Deep learning accelerates and facilitates this process.

In general, it is a way to automate predictive analytics. On the other hand, traditional machine learning algorithms are linear, and deep learning algorithms are built on layers of increasing complexity. 

To understand deep learning, imagine a child whose first word is a cat. Young children learn what a cat is and what it is not by pointing at something and saying the word cat. Parents say “yes, it’s a cat” or “no, it’s not a cat.” As the child keeps pointing at things, he notices the characteristics that every cat ​​has. This is the mechanism of deep learning. 

Computer programs that employ deep learning go through the same process that children learn to identify cats. Each algorithm in the hierarchy applies a non-linear transformation to the input and utilizes what it learns to develop a statistical model as output. The iteration continues until the output reaches an acceptable level of accuracy. The number of layers of processing that data must pass through is the reason it is called Deep.

Models Methods

You can use a variety of methods to create powerful deep learning models. These techniques include reduced learning rates, transfer learning, training from scratch, and dropouts:

Learning rates: The training rate is a hyperparameter that defines the system before the training process or sets the conditions for its operation, controlling the amount of change the model receives depending on the estimation error each time the model weights are changed. If the learning rate is large, the training process can become unstable, and a suboptimal set of weights can be learned.

If the learning rate is too minimal, the training process will be lengthy and can get bogged down. The learning rate annealing method is the process of adjusting the learning rate to improve performance and reduce training time. One of the simplest and most common adjustments to the learning rate during training is to reduce the learning rate over time. 

Transfer learning: This process involves the completion of a previously trained model. You need an interface to the inside of your existing network. First, users provide existing networks with new data, including previously unknown classifications. 

Once the network has been tuned, you can use the more specific classification features to perform new tasks. This method requires far less data than other methods and has the advantage of reducing computation time to minutes or hours.

Training from scratch: This method requires developers to collect large labeled datasets and configure a network architecture that allows them to learn features and models. This methodology is especially useful for new applications and applications with many output categories. 

But overall, it is less common. This is because it requires an excessive amount of data, which can take days or weeks to train. This strategy attempts to solve the problem of overfitting in networks with large parameter sets by randomly removing units and their connections from the neural network during training.
Dropout methods: dropout methods have been demonstrated to improve the performance of neural networks in supervised learning activities in areas such as document classification, speech recognition, and computational biology.

Virtual Machine Technology : What is it and how does it work ?

Lire plus

Even the most tangible objects, such as a machine, can be virtual! We’ve been making rapid progress in tech innovation thanks to the brilliant talents. This creates countless opportunities for businesses to thrive and amuse their clients.

This article provides answers to your questions about virtual machine technology and recommends best practices to maintain an effective deployment.

What is a Virtual Machine (VM)?

Simply put, a virtual machine (VM) is a digital environment that functions like a computer within a computer. This technology runs on a separate partition of the host server. Therefore VMs deploy their CPU power, memory, operating system, and other resources. In other words, you can benefit from a full computer capacity without the need for additional hardware.

How Does VM Work?

This technology is possible with the power of Digital and virtual solutions. The process depends on the software that simulates hardware equipment. Therefore, you can run as many VMs as you need on your host server.

VMs exist thanks to a hypervisor, a software that oversees this operation. In fact, hypervisors empower Operating systems to increase hardware capabilities, enhance reliability, and reduce costs. Moreover, they allow operators to

  • Boost hardware performance: A hypervisor virtualizes and shares resources so that VMs may run without interfering with host server operations. This improves the hardware’s capabilities and boosts efficiency.
  • Improve flexibility: By separating VMs from the host hardware, you can construct separate workstations. Hence, you can move the VMs to separate machines and remote virtualized servers without halting them.

Increase security: Since VMs are technically separated from one another, they do not rely on one another. Hypervisors are extremely secure because any crashes, attacks, or malware on one VM do not affect others.

Upgrade your business with virtual machine technology

Virtual machine technology enables businesses with high performance. Thanks to virtual desktop infrastructure (VDI) your team can access desktop environments or open-source operating systems remotely.

Moreover, VDI functions as a digital office, accessible at any time and from any location. Therefore, your team will be more productive by providing simple access to corporate products. Aside from cost savings, security, and scalability, virtual machines provide numerous other advantages to organizations.

What are VMs used for?

Virtual machine technology offers a range of useful applications. Here are a few applications for virtual machines:

  • Developing and deploying Cloud-based apps.
  • Exploring new operating systems (OS).
  • Assisting developers with simpler and quicker dev-test scenarios.
  • Running applications regardless of the OS.

Apps for Virtual Machine technology:

There are various virtual machine programs from which to choose:

VirtualBox is a virtual machine program that operates on Windows, Linux, and Mac OS X. VirtualBox is popular thanks to its open-source nature. Since it is a completely free tool,  you will not be forced to the standard “upgrade to gain more features” adds. VirtualBox performs admirably, especially on Windows and Linux, where there is less competition, making it an excellent beginning with VMs.

VMware Player is another virtual machine technology that operates on Windows and Linux. In fact, VMware produces its virtual machine software and runs freely on Windows and Linux. However, to get advanced services, you need to get the premium VMware Workstation program.

VMware Fusion and Parallels Desktop offer unique solutions. In fact, they are dedicated to Mac users that want to run Windows applications. The market is rich with virtual machine options, including KVM for Linux and Microsoft’s Hyper-V, for businesses.

For better results, perform a comprehensive assessment of your IT infrastructure before deploying virtual machine technologies.

Virtual Machine Types:

Companies can leverage one of two VMs types:

VM Processing: also known as a VM application and it allows a single process or program to operate on a host server. This type allows businesses to deploy programs on any OS they have on their host device. In other words, you can create a platform-independent environment. Examples of this type are Java Virtual Machine (JVM) and Wine software.VM Systems: also known as hardware VMs, provide virtual operating systems (OS) and replace an actual machine. In this model, the physical resources of the host server are shared. However, they run separate operating systems. Examples of this include VirtualBox and VMware ESXi.

Challenges you may face:

Although VMs offer excellent work environments, it is not all sunshine and rainbows. In fact, your company may face some challenges, including:

  • When many VMs operate on the same host, the performance of each might vary according to the system’s workload.
  • Licensing models for VM systems might be challenging. Therefore, they may lead to expenses.
  • Security is a growing problem due to the rising number of breaches on VMs and cloud installations.
  • The infrastructure configuration for any VM system is complicated. Small firms must recruit specialists to properly implement these solutions.
  • A data security risk can occur when numerous users attempt to access the same or different VMs on the same physical host.

Virtual Machine in Cloud Computing

Virtualization and Cloud computing are connected at the hip. To take advantage of hybrid Clouds, businesses may create Cloud-native VMs and transfer them to on-premises servers.

Cloud services may also be adjusted to adjust various demand levels. This enhances scalability not just for end-users but also for your teams. Developers, for instance, are enabled to establish ad hoc virtual environments in the Cloud to test their solutions.

Moreover, the host server can distribute resources across several guests with the use of VMs in Cloud Computing. However, each will have its own version of the operating system. This immediately provides a great environment for evaluating other operating systems, such as:

  • The production of operating system backups
  • Access to virus-infected data
  • Beta releases
  • Running software or applications on operating systems that were not previously considered.

Cloud VMs for Windows:

Azure, Microsoft’s own cloud service provider, offers several services for software developers, including VMs. As a cloud service, VMs in Azure Cloud Computing allocates numerous images on the cloud platform, making deployment rapid and effective.

Requirements for building a cloud Virtual Machine on Windows 10:

  • A solid and safe internet connection.
  • RDP software.
  • Edge or other browsers.
  • An activated Azure cloud account.


VMs are indeed the fruit of innovation. They offer greater support and they hold many benefits for businesses. In this article, we defined VMs technology and highlighted their benefits. Moreover, we offered recommendations for businesses that consider VMs solutions.

Artificial Intelligence in Cybersecurity

Artificial Intelligence in Cybersecurity : How to build a solid Security System

Lire plus

The bright tech talents have given us Artificial Intelligence (AI), and you wonder how to use it in Cybersecurity. 

Technology is at a crossroads as Machine Learning and Artificial Intelligence evolve faster than before. As a result, computer systems that apply Artificial Intelligence are becoming vital and prevalent.

These new talents can make businesses operate effectively with huge potential. For example, developers may introduce Artificial Intelligence (AI) systems to:

  • Create danger warnings.
  • Discover new forms of malware.
  • Secure important data for businesses.
  • And many more features.

The goal of Artificial Intelligence is to imitate human intelligence. Therefore, (AI) is constantly learning from previous and current incidents to identify new types of threats that might happen.

This article will answer your concerns about the efficiency of Artificial Intelligence in Cybersecurity. After profound research, we gathered the most frequent questions, and we are here to offer satisfying answers.

What is Artificial Intelligence (AI)?

AI builds intelligent computers with human aspects. It creates machines with natural intelligence to learn and replicate human actions. In other words, machines will learn, gain experience, and do human-like tasks.

These exciting features made AI the new tech trend, and businesses want to be engaged with AI and machine learning. Indeed, as AI technology progresses, it impacts our quality of life.

Understanding AI technology depends on where you are looking and who you ask:

  • Someone with a passing knowledge of the technology would associate it with robots. They’d describe Artificial Intelligence as a Terminator-like figure capable of acting and thinking for itself.
  • AI researcher will tell you that it is a collection of algorithms that can create outcomes without being explicitly directed to do so

Can Artificial Intelligence create machines that behave like humans in Cybersecurity?

Unfortunately, this is not a yes/no question, and no one can give you a one-word response. However, we can simplify things and help you make sound decisions.

Natural AI Machines Can Earn

1. AI Improves Over Time

As the name implies, AI technology is intelligent, and it uses this capacity to improve network security over time. In fact, AI can

  • Employ machine learning and deep learning to understand the behavior of a business network over time.
  • Detect and groups network patterns.
  • Then, identify any deviations or security issues from the norm before responding to them.

The patterns that artificial neural networks learn over time can improve security in the future. Since AI is always learning, Potential threats comparable to those recorded are identified and stopped on time.

2. AI Is Capable of Handling Big Data

Businesses generate a lot of traffic, and many activities happen on a company’s network. Therefore, you need a tool to protect your data from harmful individuals and software. However, cybersecurity specialists are limited in scanning all communication for potential threats.

Artificial intelligence is the best approach that uncovers hidden threats. AI can sift through vast amounts of data and traffic thanks to its automated nature. A home proxy, for example, can assist you in data transfer. Moreover, it can also detect any risks buried in the chaos.

3. AI Eliminates Redundant Processes

AI simulates the best human qualities while avoiding errors. It offers a range of key practices, including:

  • Handling redundant cybersecurity processes that might weary your cybersecurity team.
  • Assisting in the routine detection and prevention of fundamental security risks.
  • Thoroughly examine your network to see whether any security flaws might be detrimental to your network.

As previously stated, attackers frequently vary their strategies. However, Artificial Intelligence in Cybersecurity offers the best practices.

Artificial Intelligence deficiencies in Cybersecurity

AI has flaws like any other technology since it is not inherently intelligent. However, the most severe issue is the “Sorcerer’s Apprentice” problem. This refers to the risk of initiating activities that are no longer within human control.

AI cannot automate any task that needs a certain level of human intelligence. Because AI is not inherently intelligent, automation is restricted to repetitive activities. AI, like any other technology, has drawbacks. The most serious is known as the “Sorcerer’s Apprentice” problem. This refers to the risk of initiating activities and effects that are no longer within human control.

  • Random modifications in program code: polymorphic viruses have demonstrated this capability, but AI can expand the number of available variables to a new level.
  • Adapting to operating systems: In order to avoid being identified, the AI-based virus might develop an intelligent approach to Kernel-level functions or use rootkits.
  • Recognizing and fighting antivirus software: an AI-enabled virus may identify antivirus software and design strategies for attacking its code.
  • Social detection: viruses might utilize conversational programming and face recognition technologies to imitate human discourse. Therefore, it can easily deceive individuals into transmitting confidential documents, handing over access details, or just cyberbullying.
  • Creating updates: Some viruses can adopt detection methods. Therefore, they can release a new version and continue its destructive operations.

The Most Critical Concerns in Cybersecurity:

The active use of machine learning may not be the only issue that companies and cybersecurity experts must tackle. Some are the outcome of inadequate security strategy.

  • Legacy infrastructure: Systems today interact across continents, delivering critical data worldwide. These transactions are not adequately protected and are easy to break. Therefore, if you use old Infrastructure, you increase your vulnerability.
  • Manual detection: Teams cannot focus on security risks and suspicious trends 24 hours a day, seven days a week. Most of the time, systems go unmonitored, and errors are likely to happen.

Proactive policies: Most security specialists are more concerned with dealing with attacks than forecasting them.

Predictive (AI): Impact in Cybersecurity

Predictive AI is a strategy based on statistics. This tool takes data, analyzes it, and recommends avoiding different cyberattacks. Moreover, it allows analysts to make assumptions and test records to estimate the likelihood of a specific future result.

predictive AI is sometimes known as “third wave AI.” It was first introduced by the Defense Advanced Research Projects Agency (DARPA) as an intelligent tool. In fact, Third-Wave AI is utilized in Security Operation Centers (SOC) and works in real-time to protect against data breaches, malware, and ransomware attacks.

Types of AI algorithms that you can leverage for cybersecurity purposes:

Supervised AI algorithms: Human supervision is required for this module of AI algorithms. The algorithm is created by analyzing data patterns to keep the network and data safe.

The method is similar to how you would train a toddler. For example, you may display several symbols and explain what each represents. They can then recognize the relative information when you ask difficult questions about any random symbol in the same database.Unsupervised AI algorithms:  without human supervision, this model can predict ways. It is a self-learning strategy in which algorithms train and uncover data patterns difficult for people to find.

A generative model is an example of this model. It offers an unsupervised learning strategy in which algorithms mimic the production of training data. In fact, it duplicates data from previous intrusions to avoid future risks. 

Reinforcement AI algorithms: This learning method differs from the previous two algorithms. In fact, you are not required to provide training patterns to the algorithm. Instead, you offer guidance or an approach for improving performance in certain instances. Without the need for human interaction, the algorithms may be trained for an endless number of possibilities.

A predictive AI development creates intelligent cybersecurity practices that detect risks and prevent attacks.

Since old infrastructure cannot face today’s challenges, AI technology offers the needed tools to detect, anticipate and fix security issues.

AI & ML: Applications in Cybersecurity

Artificial Intelligence (AI) and Machine Learning (ML) ‘s growing popularity makes them significant actors. In fact, ML has several applications in Cybersecurity, including:

  • Cyber Threat Identification.
  • AI-based Antivirus Software.
  • User Behavior Modeling.
  • Fighting AI Threats.
  • Email Monitoring
  • and so on.


Keeping your data and network safe nowadays is a challenging mission. Therefore, adopting AI and ML can help you improve your cybersecurity tools. However, it would help to consider several considerations before investing in these tools.