Over the past couple of years, there has been an explosion in the field of machine learning (ML). Many companies are using ML to automate tasks for their products or services.
Many people have heard of AI (artificial intelligence) before, but not much clarity is given as to what it actually means. Some say that it is intelligent computers, while others describe it as robots that learn.
A very popular form of AI at the moment is called “machine learning” (or sometimes just “learning” technology. It was first coined back in 1980s, however, it wasn’t until around 2014-2015 when it got its big push due to the availability of large amounts of data.
This article will talk about why deep neural networks are so attractive, and how they work under the hood. But first, let us look at some examples of things that use machine learning now!
Examples of applications
There are many ways that businesses use ML these days. Here we will discuss three types of applications, and how each one works.
1. Product recommendation engines
These take information from your product catalog or list and search through databases of other products to determine which ones would be best for new customers. For example, if you sell vacuum cleaners, then Amazon can compare prices between different brands and find better deals elsewhere to recommend them to potential buyers.
Definition of deep learning
What is machine learning? And what are some uses of it? That’s definitely something we can talk about!
Machine learning (or, more accurately, artificial intelligence) is the field that studies how computers can learn to perform tasks or functions for themselves.
Tasks can include identifying objects in images, translating languages, predicting disease outcomes, and so on. Technically speaking, anything beyond just performing simple calculations like math is considered “artificial intelligence.”
But most people refer to AI as only applying to computer programs that use algorithms to solve complex problems by gathering data and then changing their behavior based on what they have learned.
That’s the classic definition. But this narrows down the applications way too much. It also ignores one of the biggest strengths of AI: its ability to grow and develop over time.
Computer scientists now hope to take advantage of this potential growth by teaching AI systems called neural networks using lots of examples of different issues and situations. These lessons are then applied to new instances of the same problem domain.
This concept is known as pre-training, and it’s an essential part of state-of-the-art AI. Almost every successful application of AI today includes at least some kind of pretraining.
What is the difference between the two?
Technically, deep learning is not machine learning. They are different terms with similar-sounding names that describe very different concepts.
Deep learning is actually much more than just machine learning. It’s a way of thinking about how to solve problems. When you apply advanced neural networks to computer vision or natural language processing tasks, that technique is referred to as MLP (multilayer perceptron) or ANN (artificial neuron network), respectively.
But it’s important to know the fundamental differences between these types of algorithms before choosing one over the other.
In this article we will talk about why using deep nets can be better than using traditional ML methods for solving certain classification and regression problems. But first, let us discuss what makes up the basic structure of both deep and non-deep net architectures.
Deep learning is better at
Recent developments in deep neural networks have shown that they can be improved upon through what’s called reinforcement learning.
Traditional machine learning algorithms learn from examples, creating patterns based on past experiences. But this process doesn’t work when there are no clear examples to compare with because systems constantly change.
Reinforcement learning changes the algorithm so that it learns not just one behavior, but many. These individual behaviors are referred to as policies.
A policy is like a set of rules that tell an AI how to behave. For example, your smartphone uses a policy to determine which app to open next or whether it should ask you to add more accounts for a given service.
Learning to perform tasks
A lot of people get stuck thinking about AI as a technology that can learn from data how to perform specific tasks, such as taking pictures or playing games. This is what we often refer to as machine learning (or more commonly just ML).
But this isn’t the only way that AI could be used! In fact, there are many ways that AI could potentially change our lives for the better by helping us achieve things beyond just teaching computers new tricks.
A good example of this is when you look at something like Amazon Prime. While it may seem frivolous to pay monthly fees for free shipping and early access to movies and TV shows, it actually gives you quite a bit of value.
Prime comes with a subscription to their Cloud Computing service which allows you to create digital documents, store files in the cloud, and even have computerized proofreading and transcription services. All of these things cost money, but being part of the Amazon Prime family means that they are paid for automatically via your membership.
This is similar to how most major smartphone companies now offer phone plans where you pay a one-time fee for the device and then an ongoing price per gigabyte of mobile storage. It costs money, but creating digital content and having easy access to well-paying jobs makes it worth it.
There are other examples too, like self-driving cars that can navigate traffic without human intervention.
Analyzing big data
Many people get stuck using machine learning because they believe it only works for analyzing large amounts of data. This is not true! While there are some applications that use ML to analyze lots of information, this is not how most AI systems work.
In fact, there’s no reason you can’t start experimenting with deep learning right away! You don’t need tons of data or even past experience in ML to get going.
What you do need though is a way to evaluate your model’s performance. Luckily, there are several methods available these days!
We will discuss three common ways to do this in the next few pages. Make sure to check out our companion article first before moving onto the discussion!
Systematic Model Evaluation – also known as Accuracy-Based Metrics or Error Rate Measures – are one of the best approaches when evaluating models.
These metrics compare predicted results against a set truth or label. By doing so, you can determine whether or not your model was successful and what changes may need to be made next time around.
One of the major differences between traditional machine learning and deep learning is how each method learns from data. Traditional algorithms rely on defining features or descriptors for input data sets, applying mathematical functions to determine if there are pattern changes in those features, and then using these patterns as predictions for new data.
Deep neural networks do not have this concept of feature extraction. Rather, they learn what information is contained in the data set automatically and apply it directly towards prediction. This seems more intuitively logical, but requires much larger datasets to work effectively.
Traditionalists often argue that feature engineering is off limits because it goes against the very nature of ML, where math formulas tell you something about the data. With deep learning though, engineers can add, remove, and re-arrange layers to identify important features and find relationships that would otherwise be overlooked.
There is a common misconception about what makes AI “smart” or not. Some people seem to believe that making computers more intelligent means giving it large datasets and letting it learn from its mistakes, which are also known as error-based learning.
Error-based learning works for certain types of tasks, but this isn’t true intelligence. For example, telling someone with very little knowledge of geometry how to do trigonometry would be using error-based learning to teach them math.
Computer programs already use some forms of error-based learning. When you take your car to get fixed at a shop, they may test run the engine by turning it over and looking at the flow of air through the vents and smells coming out of the exhaust. If everything looks okay and the smell goes away, then they can sometimes conclude that there was no problem with the airflow.
This kind of testing is called diagnostic reasoning, and many computer systems now have modes where they apply this principle in order to see if their software is working correctly. A popular example of this is when self-driving cars check themselves out by scanning traffic signals, roads, and other vehicles before changing lanes or pulling into a parking lot.
There’s nothing wrong with using error-based learning in these settings, but it shouldn’t be our ultimate goal for creating artificial general intelligences.
Deep learning is used in
Neural networks are one of the most powerful types of machine learning algorithms out there. Technically, they’re not even considered to be “machine learning” since they don’t use any defined feature sets or mathematical functions.
Instead, neural nets learn features and patterns on their own by feeding them large amounts of data. This makes them very efficient at finding patterns where other methods may fail.
Deep learning has become increasingly popular due to its success in applications like computer vision and natural language processing (NLP). Over the past few years, it has completely transformed the field of AI.
At this point, almost every company that wants to create an intelligent device will need some form of deep learning. Even if you aren’t creating gadgets, people have been using it for academic research projects for quite some time.
That’s why it’s important to understand what the term actually means, how well it works, and whether it’s truly better than other ML techniques. In this article, we’ll talk about the differences between classic ML strategies and deep learning, explain when each technique is appropriate, and conclude with our opinion on which one is more effective.
Disclaimer: The content in this article should not be construed as financial advice; instead, it should be seen as information and insights into different investment philosophies. Please do your homework before investing money in anything.