Difference between Deep Learning and Machine Learning

Difference between Deep Learning and Machine Learning:

We need to focus on in this chapter is the idea of deep learning and machine learning. To someone who hasn’t had much of a chance to explore these topics and some of the processes that come with them, these two terms are going to seem like they are identical and that we can use them in an interchangeable manner. But in this section, we are going to explore these a bit more and see how they are similar and how they are different.

One of the most common techniques in artificial intelligence that can be used to help us process some of that big data we have been collecting for a long time is known as machine learning. Machine learning is going to be an algorithm that is self-adaptive. This means that it is able to learn from what has happened in the past and can work on making increasingly better analyses and patterns with data that is newly added over time, and even with some of its own experiences.

There are a lot of examples of how this would be able to work in the real world, and there are a lot of companies who already work to make this happen for their needs. Let’s say that we take a look at a company that handles digital payments. One of their main concerns is to keep the levels of potential and actual fraud from occurring in the system. Instances of fraud can cost them millions of dollars a year, if not more, and being able to catch these ahead of time and understanding how to prevent these before they happen could be a lifesaver for most of these financial companies.

The good news is that these digital payment companies could employ some tools of machine learning for this kind of purpose. The computational algorithm that would be added to your computer model will work to process all of the transactions that happen on our digital platform and can make sure that we find the right patterns in any set of data. If it has been able to learn in the right manner, the algorithm will be able to point out any anomaly and more that is detected in this pattern.

Deep learning, which is a subset of machine learning, can work in a similar manner, but it does this in a more specific way and includes specific machine learning algorithms to get things done. When we talk about the deep learning process, we are looking at a form of machine learning that will utilize a hierarchical level of artificial neural networks in order to make sure that the process of machine learning is carried out properly.

These artificial neural networks are going to be built up much like the human brain, and there are nodes of neurons that will connect with one another similar to a web. These nodes are able to send information to each other and will communicate with one another to make this process work well.

Unlike some of the traditional programs that are going to work in a linear way to build up a new analysis with the data at hand, the hierarchical function that comes with a system of deep learning is going to enable our machines and any systems that we use with this process to go through the data with an approach that is more nonlinear.

Let’s go back a bit to that example that we did with fraud protection with an online payment company. If this company worked with a more traditional approach to detecting things like money laundering and fraud they would rely on the system just picking up on the amount of the transaction. Related article: Machine Learning Vs Deep Learning with example

This means that they would only catch the issues when there was a large amount of money taken out, and maybe a few times if there was a really strange location that didn’t make sense to where the person was located. We can see where this can run into some troubles because not every instance of fraud or money laundering is going to include big amounts, and most people who try to do these tasks are going to stick with smaller amounts to stay under the radar.

But when we work with deep learning, we are able to work with a technique that is seen as nonlinear, and this would include things like the time of the transaction, the type of retailer that is being used, the IP address, the geographic location of the user and when the transaction happens, and any other features that the company would like to rely on, in order to point out when a transaction is likely to be fraudulent. The first layer that comes with this process of the neural network is that we will take the raw data and input it. This could include something like the amount of the transaction. Then this is turned over as the output of that first layer. The second layer will use that output to work on itself, and may include some additional information like the IP address, and will pass all of this as its results or output as well.

Then we move on to the third layer. This layer is going to take all of the information from the second layer, and include some more raw data, like the geographic location, and will make the pattern of the machine even better from here. This will continue through all of the layers that the programmer set up with the neural network until the system can determine whether the transaction is legitimate or fraudulent.

Remember here that deep learning is going to be considered a function of artificial intelligence that helps us to mimic the workings of what happens inside the human brain, at least when it comes to processing through a lot of data to make important decisions. Deep learning with artificial intelligence can be useful because it can learn from the data, whether we have data that is labeled, or if we are working with data that is both unlabeled and unstructured. Deep learning, which is a subset of popular machine learning, can be used to help out almost any industry that we can think of, but in the example that we talked about above, it is especially helpful with detecting things like fraud and the laundering of money.

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