5 Steps for Getting Started in Deep Learning
While the math and the development of a functioning AI model are extensive, the general idea can be broken down into easier steps to learn how you can get started on your journey. Let’s go over the basics of where to start to grasp the complex topic of artificial intelligence and deep learning.
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5 Steps for How to Learn About Deep Learning
Learning about deep learning methods and technologies has made a surge with new powerful models displaying capabilities we have never seen before. AI models built for the average user like ChatGPT and DALLE-2 have brought a mainstream spotlight on artificial intelligence.
Understanding the inner workings of deep learning can be as confusing. While the math and the development of a functioning AI model are extensive, the general idea can be broken down into easier steps to learn how you can get started on your journey. Let’s go over the basics of where to start to grasp the complex topic of artificial intelligence and deep learning.
What Is Deep Learning In One Sentence?
Deep learning is a way for computers to learn and make decisions on their own, by training on large amounts of data and using complex neural networks that mimic the structure of the human brain to perform complex tasks.
The goal of deep learning is to take information that humans could take in manually, over a huge scale, and generate expected results based on that information. Imagine parsing through a large data table to find a commonality. While it’s tedious to check every data point manually, an AI algorithm can detect patterns and make assumptions to perform various tasks you instruct it to.
The overlapping layers of coding and programs that process this data can be called a neural network, similar to how the human brain consists of billions upon billions of neurons to create a biological computer system, in a sense. Deep learning simply takes that human brain function and applies it to computer science: billions and billions of connecting neurons via code instead of electrical impulses.
Can You Learn Deep Learning on Your Own?
Yes! You can learn deep learning completely and totally on your own, but it will take significant time and effort if you are starting from absolutely no knowledge about code, data tackling, or linear algebra and calculus.
Most people who are interested in how to learn about deep learning have some working knowledge of one or all of these subjects, however. It is highly unlikely that you don’t already have some prior knowledge that will help you figure out the best way to learn deep learning skills.
If you can work on these skills over the course of 6-12 months by chipping away at learning these concepts in 5-10 hours every week, then you could be programming your own deep-learning models within a year!
The next section will go into a comprehensive guide on exactly what you need to learn, how to start with machine learning and move into deep learning, and some recommendations for your learning along the way.
How to Get Started Learning About Deep Learning
As already stated, you will need to become comfortable and familiar with linear algebra and calculus, tackling and formatting large amounts of data, and coding within a multitude of frameworks in order to figure out how to learn deep learning.
Once you feel confident in your ability to work through those challenges, you are truly halfway to being prepared for your own machine learning and deep learning endeavors. After that, you will need to focus on getting started,
Step 1: Set Up Your System Properly
Once you have the basic principles locked in, then you will want to focus your attention on getting your computer system set up to handle deep learning modeling. Now, what does this have to do with how to learn deep learning? Well, it is actually a vital step because, as you’ll see in Step 2, you are going to need to practice!
If you need some guides on how to make sure you have everything set up for a system that is ready for machine learning and deep learning, then check out all of the articles we have on the parts you might need for this particular build.
Deep Learning is synonymous with High-Performance Computing but in this day and age, a serious deep learning workstation and laptop when starting out are not entirely necessary. You can start out with smaller datasets on your desktop and graphics card, or leverage cloud computing.
Testing proof of concepts with smaller data sets with deep learning, expect some inaccuracies. Once you have validated your skills, look into building or purchasing your very own system.
Step 2: Get Started By Working on Deep Learning Models
To understand the best way to learn deep learning, then you need to understand that it is simply getting started on work involving deep learning models that help the most.
Much of what we learn is by performing the action, correcting our mistakes, and then gaining deeper knowledge along the way. For example, we don’t start to learn to ride a bike by sitting down and understanding how gears work, what a sprocket does, and Newton’s Laws of Motion.
No, you got on the bike and tried to start pedaling! Then you likely fell down, got back up, learned from your mistakes, and tried again. Apply this concept to the first time you learned to cook or use Google’s search engine. You’ll see we learn by understanding enough to get started and then figuring out the rest along the way.
This is the first step that trips everyone up. The secret to knowing how to learn deep learning skills? Getting started.
Step 3: Study Machine Learning and Deep Learning Theory
If you really want to know how to learn machine learning and then how to learn about deep learning, you are going to want to make sure you study machine learning and deep learning theory.
This is where you will start to learn some of the major nuances and can begin building your knowledge base on top of the skills you have already developed by simply getting started. Being a good student on these essential topics is how to learn deep learning at a much higher level.
For some excellent courses on deep learning theory, I recommend:
- Deep Learning Specialization on Coursera
- MIT’s Intro to Deep Learning
- Fast.ai’s Practical Deep Learning For Coders V3
There are also various tutorials on Youtube and blogs that can be beneficial when you have got the basics dialed in. Deep Learning is a dense topic that you learn as you go.
Step 4: Build Your First Deep Learning Model
The best way to learn deep learning is to work towards a goal. As you are getting started and gaining more knowledge, it is time to start building your own deep-learning model.
This can look completely different based on what kind of project you may want to work on, but don’t try anything too complicated just yet. Start small and build your way up, making sure to avoid common machine learning and deep learning mistakes along the way!
Step 5: Grow, Improve, and Keep Learning About Deep Learning
The final step in how to learn deep learning is to simply keep learning. Become a student of machine learning and deep learning, and keep building your own models and exploring what others have created. Try new models, solve new problems, and tackle new projects.
If you are serious about deep learning, then take the next step and try for an internship or even a career in Deep Learning Development!
Looking for More Information on Deep Learning?
Understanding how deep learning works can seem like an overwhelming task, but with the right direction, it is more than manageable! The industry of AI and deep learning development is growing every year with some seeing it as a “future skill” that will only become more needed as time goes on. So whether you want to learn deep learning for fun or for a potential career, there will be plenty of opportunities in the future.
Published at DZone with permission of Kevin Vu. See the original article here.
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