You’ve definitely seen all the news articles talking about how AI is going to revolutionize every single industry. Every article cites the same AI tools: ChatGPT, DALL-E, Midjourney, and Stable Diffusion. Well, guess what? Every single one of these tools is powered by a deep learning model. Deep learning is taking over the industry, so you need to understand what deep learning is if you want to become an AI professional.
News articles need to cater to a wide audience, so they usually gloss over the how and why of these deep learning models and only talk about what they do. To be fair, what these models do is extremely impressive; however, what we want to do is learn more about the secret behind the magic so that we can make our own models to solve our own specific problems. Deep learning is not something that can be mastered in a day, but few things worth learning are!
Join the wave of professionals who are redefining what AI can do by diving into the world of deep learning. Whether you want to make a career change or just want to take your business to the next level, you’re going to need to understand the basics of deep learning if you want to stay relevant.
In this article, we’re going over some deep learning foundations so that you can be equipped with the knowledge you need to navigate the world of deep learning and begin training your own models.
What is Deep Learning?
Deep learning is a subset of artificial intelligence that has gained a lot of popularity over the past few years due to the kinds of problems that it can solve. At its core, deep learning seeks to automate the human process of learning in order to create models that can mimic the brain’s ability to make decisions.
The premise that led to deep learning is that traditional programming techniques worked very differently from how the brain functions, which is why certain things that were very easy for the brain to do (categorizing objects, understanding language, etc.) were very hard for computers to do. Therefore, in order for us to make a computer program that could recognize objects or process language, we should create a program that functions more closely to how the brain works.
How does deep learning work?
Like we just mentioned, deep learning seeks to emulate how the brain works. To best illustrate what this means, let’s go over an example:
Say you want to teach someone to recognize a horse.
You could start by saying that a horse is an animal with four legs, but that would be insufficient.
You could then refine your definition by saying that horses have hooves, which would eliminate many four-legged animals.
You could then further say that horses have hair growing along their neck and long tails.
Surely, there can’t be that many animals left, but giraffes, zebras, and donkeys all meet those definitions.
In short, it can be difficult to teach someone (or something) to recognize an object through procedural definitions. Some workloads, such as object recognition, are easier to achieve by providing examples and having someone learn on their own. The principle behind machine learning is exactly that: instead of giving the computer step-by-step instructions on how to solve a problem, why don’t we give the computer examples of the problem and let it figure out how to solve the problem on its own?
It turns out that it takes a large amount of processing power in order to train a computer to do a task by providing examples instead of giving it step-by-step instructions. Early attempts at deep learning failed partly because their computers simply weren’t powerful enough to do the things we wanted them to do. But, modern technological advancements, including the invention of the GPU, has finally allowed us to be able to use deep learning techniques to tackle all kinds of problems.
What are neural networks?
A neural network is a specific kind of machine learning model that, in theory, is flexible enough to solve any problem thrown at it as long as it is able to be trained appropriately. A neural network is constructed of groups of neurons that are called layers. The neural network works by taking an input and passing it to the first neuron layer, which then processes the input and passes it on to the next layer for processing. With each layer, the neurons add more information to the input and are able to detect higher-level patterns. By connecting many layers of these neurons together, a network is then able to solve complex problems.
To illustrate how a neural network works, let’s consider an image recognition model with four layers:
If we give the model a picture of a car, the first layer might only be able to recognize diagonal, horizontal, and vertical lines in the picture.
The second layer could then build on the information added by the first layer to be able to recognize curves, corners, and circles.
The third layer takes that information and is able to recognize wheels, windows, and sideview mirrors.
Using all the information from previous layers, the fourth layer is then able to detect that the input picture is that of a car!
Deep learning applications
Now that we know what deep learning is and how neural networks work, we should also discuss the kinds of things that we can do with this machine learning technique. In truth, the possibilities are endless! Here are some of the problems that engineers are solving with deep learning today:
Natural Language Processing (NLP): we can use deep learning to help computers better understand human languages. Chatbots such as ChatGPT, digital assistants such as Siri, and apps such as Google Translate are all using NLP to provide users with an experience that wasn’t possible before machine learning.
Image generation: we can use deep learning to have a computer generate an image from text or for a computer to restore or improve images. Tools such as Midjourney and Stable Diffusion are leading the industry in generating images from user prompts, and Photoshop now has several tools that can make images sharper, remove objects from the background, or even detect and replicate patterns in a picture.
Recommendation Systems: many of the apps millions of people use every day, from Netflix to Instagram to Amazon, all use deep learning to provide recommendations to their users. The more a customer uses the website, the more information the deep learning model has and the better the recommendations become.
The potential of deep learning is vast and as more and more companies choose to invest their time and resources into discovering what neural networks can do for them, the need for skilled deep learning engineers is rising. This field is just taking off and if you’re interested in learning the right skills to set yourself up for success, consider entrusting Ironhack with your tech journey.
In just a matter of months, you’ll learn the necessary skills to enter the tech industry, creating the career path of your dreams.
Check out Ironhack’s Data Science & Machine Learning Bootcamp, Artificial Intelligence Bootcamp, or new AI School to find the best fit for you.
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