The term to describe storage systems that function at high speeds is primary memory.
Answer:We start each project to get some business benefits. We design it to achieve users and other stakeholder’s satisfaction. And we build it to improve organization KPIs. But, we live in a world where the project faces many uncertainties. These uncertainties or risks can prevent from achieving our project goals or objectives. So, it is critical that we identify them in time to take care of their effective responses.
The more we know our risks, the more we can evaluate and prioritize them timely for:
Reducing their probable negative impacts, or
Increase their likely positive impacts
We can use Qualitative Risk Analysis and Quantitative Risk Analysis techniques to evaluate and prioritize risks. I see there are a lot of confusions around how these two techniques are different from each other. In this blog, I will address these confusions and differences between these two techniques.
Before we get into the difference between qualitative and quantitative risk analysis/assessment, it is mandatory to understand how we perform risk analysis in projects. Below is the summarized demonstration of the risk analysis:
Explanation:
All drivers
All drivers who share the road with an impaired driver are at risk.
Answer:
Kindly check Explanation.
Explanation:
Machine Learning refers to a concept of teaching or empowering systems with the ability to learn without explicit programming.
Supervised machine learning refers to a Machine learning concept whereby the system is provided with both features and label or target data to learn from. The target or label refers to the actual prediction which is provided alongside the learning features. This means that the output, target or label of the features used in training is provided to the system. this is where the word supervised comes in, the target or label provided during training or teaching the system ensures that the system can evaluate the correctness of what is she's being taught. The actual prediction provided ensures that the predictions made by the system can be monitored and accuracy evaluated.
Hence the main difference between supervised and unsupervised machine learning is the fact that one is provided with label or target data( supervised learning) and unsupervised learning isn't provided with target data, hence, it finds pattern in the data on it's own.
A to B mapping or input to output refers to the feature to target mapping.
Where A or input represents the feature parameters and B or output means the target or label parameter.
Answer: The goals of computer security are to protect computers and users from data theft or loss as well as damage to any part of the computer.
Explanation: Common means of achieving computer security are firewalls, anti-virus software and this can fail due to hardware problems or
weaknesses that prevent malicious attacks.
To answer this question, think of a time when you experienced any one of these. For example, personally, I was once an unfortunate victim of a general malicious attack that took advantage of a weakness in my anti-virus software. After clicking on a link on a dodgy website, a virus was installed on my computer. My computer finally crashed, without any hope of restarting it. I lost all my data and I had to buy a new computer. This was a malicious attack.
However, sometimes people can be specifically targeted to steal their data or monitor their activities.
