While we are on this topic, we also need to spend some time looking at machine learning and how it is able to fit in with the topics of deep learning and data analysis. Machine learning is another topic that is getting a lot of attention throughout the business world, no matter which industry you spend your time in, and no matter how you make this happen, and the importance of machine learning and other parts of artificial intelligence in your project and the models that you create.
What Is Machine Learning?
The first thing that we need to take a look at here is the basics of machine learning. This is going to include a lot of algorithms that are able to first parse the data we have, learn from that data, and then apply what they were able to learn from that data to make a more informed decision. Basically, it is a process we can use in order to teach our machines and programs how to learn and make important decisions on their own.
Let’s take a look at an example of how this is meant to work. A good example of this process would be a streaming service for on-demand music. For this service to stick with some decisions about which artists or songs to recommend to one of their listeners, the algorithms of machine learning will be hard at work. These algorithms are able to associate the preferences of the user with other listeners who have a similar taste in music. This technique, which is often given the generic name of “artificial intelligence,” is going to be used in many of the other services that are able to offer us recommendations in an automatic manner.
Machine learning is going to fuel all sorts of tasks that are automated and that can span across many industries. This could start with some firms for data security, which will hunt down malware and turn it off before it infects a lot of computers. And it can go to finance professionals who want to prevent fraud and make sure they are getting alerts when there are some good trades they can rely on.
We are able to take some of the algorithms that come with artificial intelligence and program them in a manner that makes them learn on a constant basis. This is going to be done in a manner that stimulates the actions of a virtual personal assistant, and you will find that the algorithms are able to do these jobs very efficiently.
Machine learning is going to be a complex program to work with, and often it takes the right coding language, such as Python, and some of the best libraries out there to get things done. The algorithms that you can create will involve a lot of complex coding and math that can serve as a mechanical function. This function is similar to what we may see a screen on a computer, a car, or a flashlight do for us.
When we say that something, such as a process or a machine, is able to do “machine learning,” this basically means that it’s something that is able to perform a function with the data you provide over to it, and then it can also get progressively better at doing that task as time goes on. Think of this as having a flashlight that is able to turn on any time that you say the words “it is dark,” so it could recognize the different phrases that have the word “dark” inside of them, and then know to continue on with the action at hand.
Now, the way that we can train these machines to do the tricks above, and so much more, can be really interesting. And there is no better way to work with this than to add in a bit of neural networking and deep learning to the process to make these results even more prevalent overall.
Machine Learning Vs. Deep Learning
Now we need to take a look at how machine learning and deep learning are going to be the same, and how they can be different. When we look at this in practical terms, deep learning is simply going to be a subset that we see with machine learning. In fact, one reality that we see with this is that deep learning is technically going to be a type of machine learning, and it will function in a manner that is similar. This is why so many people who haven’t been able to work with either of these topics may assume that they are the same thing. However, it is important to understand that the capabilities of deep learning and machine learning are going to be different.
While the basic models that come with machine learning are going to become steadily better at whatever function you are training them to work with, they are still going to rely on some guidance from you as well. If the algorithm gives you a prediction that is inaccurate, then the engineer has to step in and make sure that the necessary adjustments are made early on. With a model that relies on deep learning, though, the algorithm can determine, without any help, whether the prediction that it made is accurate. This is done with the help of a neural network.
Let’s go back to the example that we did with the flashlight earlier. You could program this to turn on at any time that it recognizes the audible cue of someone when they repeat the word “dark.” As it continues to learn, it might then turn on with any phrase that has that word as well. This can be done with just a simple model from machine learning.
But if we decide to add in a model from deep learning to help us get this done, the flashlight would then be able to turn on with some other cues. Instead of just waiting for the word “dark” to show up, we would see it work when someone said a phrase like “the light switch won’t work” or “I can’t see,” which shows that they are in need of light right then. A deep learning model is going to be able to learn through its own method of computing, which is going to be a technique that helps the system act in a manner that seems like it has its own brain. Similar two articles:
Adding the Deep Learning to the Process
With this in mind, a model of deep learning is going to be designed in a manner that can continually analyze data with a logical structure, and this is done in a manner that is similar to the way that a human would look at problems and draw conclusions. To help make this happen, the application of deep learning is going to work with an artificial neural network, which is going to be basically a layered structure of algorithms that we can use for learning.
The design of this kind of network can seem a bit confusing in the beginning, but it is designed to work similarly to the biological neural network that we see in the human brain. This is a great thing for the machine learning and deep learning that you want to do because it can lead us to a process of learning that will be more capable of hard and complex tasks than what the standard models with machine learning can do.
Of course, there are going to be times when it is tricky to ensure that the model of deep learning isn’t going to draw some incorrect conclusions. We want it to be able to work on its own to get results, but we have to make sure that we are not getting the wrong answers out of the model. And we need to fix these issues as quickly as possible. If the model is able to continue on and learn the wrong outputs and information, then it is not going to be incorrect the whole time and will not do the work that we want.
Just like with some of the other examples that we are able to use with artificial intelligence, it is going to require a lot of training to make sure that we can see the learning processes turn out the right way. But when this is able to work the way that it should, functional deep learning is going to be seen as a scientific marvel that can be the backbone of true artificial intelligence.
A good example that we can look at right now for deep learning is the AlphaGo product from Google. Google worked on creating a computer program that worked with a neural network. In this computer program, the network was able to learn how to play the board game that is known as Go, which is one of those games that needs a lot of intuition and intellect to complete.
This program started out by playing against other professional players of Go. The model was able to learn how to play the game and beat out some of these professionals, beating a level of intelligence in a system that had never been seen before. And all of this was done without the program being told at all when it should make a specific move. A model that followed the standard machine learning requirements would need this guidance. But this program is going to do it all on its own.
The part that really shocked everyone and brought this kind of technology to the forefront is the idea that it was able to defeat many world-renowned “masters” of the game. This meant that not only could the machine learn about some of the abstract aspects and complex techniques of the game, but it was also becoming one of the best players of the game as well.
To recap this information and to help us remember some of the differences that show up between machine learning and deep learning, we need to discuss some of the following:
- Machine learning is going to work with algorithms in order to parse data, learn from the data it has, and then make some smart and informed decisions based on what the algorithm has been able to learn along the way.
- The structures of deep learning algorithms are going to come in layers. This helps us to end up with an artificial neural network, that is going to learn and make some intelligent decisions all on its own.
- On the other hand, deep learning is going to be a subfield of machine learning. Both of them are going to fall under the category of artificial intelligence. But with deep learning, we are going to see more of what powers the artificial intelligence that resembles the human way of thinking in a machine.
Now, this is all going to seem a bit complicated at times because there is just so much that has to come into play to make this work. The easiest takeaway for us to understand some of the differences between what we see with deep learning and machine learning is to just realize that deep learning is a type of machine learning.
To take this a bit further and to add in some specifics, deep learning is going to be considered a kind of evolution of machine learning and will show us how this kind of technology has changed over the years. There are a lot of techniques that can show up with this kind of learning, but it often works with a neural network that we can program, and that will enable a machine to make accurate decisions without getting any help from a programmer.
Data Is the Fuel We Need for the Future
With all of the data that we see being produced in our current era, the era of Big Data, it is no surprise that we are going to see some innovations that marvel and surprise us all of the time. And it is likely that we will see a lot of new innovations in the future that we can’t even fathom yet. According to many experts who are in the field, we will likely see a lot of these innovations show up in applications with deep learning.
This can be a great thing for our future. Think of how this kind of technology is going to be able to take over the world and help us to do things and experience things in ways that we never thought possible. And just think about where this kind of technology is going to be able to take us in the future, and how it is going to change up a lot of the way that we interact with each other and with other businesses as well.
Many of the applications that we see with artificial intelligence show up in customer service. These are going to be used to help drive the advent of self-service. They can increase the productivity of the agent, and they ensure that the workflows are more reliable.
The data that is fed into these algorithms will come from a constant flux of incoming customer queries. This can include a lot of relevant context into some of the issues that the business’s customers are facing. There is already a lot of data that a business is able to collect when they work with their customers, and they can add in some third-party surveys and even ask customers for opinions in order to create some good machine learning algorithms. These algorithms then can help us figure out what the customers want, what products to release, and more.
Machine learning and deep learning are two topics that seem to be almost the same, and for those who are just getting started with this kind of process, it is hard to see how these two terms are going to be different. As we dive more into the idea of data analysis and what we are able to do with all the data we have collected, we will see that machine learning and deep learning are two very different processes that can help us do some amazing things.
Another example from IBM: https://www.ibm.com/cloud/blog/ai-vs-machine-learning-vs-deep-learning-vs-neural-networks