There is a hot debate between deep learning and machine learning. And most probably, you’ve sided with the former. Given that, you may know well that deep learning is a subset of the traditional machine learning that has become present in people’s daily lives. 

However, even if you know the advantages of deep learning over machine learning, you still need to know a few things. That said, here is a quick course to help you dive into deep learning.

Collect High-Quality Data Samples 

Humans only need a few data samples to create connections and draw conclusions. However, artificial intelligence (AI) is different, especially if you’re dealing with deep learning. For example, a regular person can easily distinguish between a pen and a pencil at a glance. 

Meanwhile, an AI needs a lot of pictures of both pens and pencils before it can differentiate between them. To have more ideas about this, you might want to go to this informative post from cnvrg.

Aside from the amount of data, you need to ensure that you’re feeding your AI high-quality data samples. This means your sample must be free from biases and anomalies that may cause your AI to reach a conclusion beyond your expectations.  

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For example, if you are taking pictures of the sky in a single day to feed an AI, you want to learn about the different types of clouds. Of course, even if you give it thousands of pictures of clouds, it will only identify the only kinds of clouds present that day.

What Developers Need To Know Before Diving Into Deep Learning
Artificial intelligence (AI), machine learning and modern computer technologies concepts. Business, Technology, Internet and network concept.

Be Systematic With Your Approach

In addition to gathering high-quality data, you need to be organized and systematic with deep learning. One way to do this is by labeling and standardizing the data you collect. Likewise, you may cleanse your data samples to ensure you’re getting the suitable high-quality samples that your AI needs.

Acquire High-End Machines With A Lot Of Computing Power

The power of AI deep learning is fascinating, and the number of possibilities with it is exponential. You can do much with it, and you should never stop with anything simple. The power of the AI you’ll create using it depends on how imaginative and ambitious you are.

Because of that, deep learning requires an AI to go through tons of data samples to achieve its goal. While personal computers nowadays can perform many computing-heavy tasks, they’re still not enough if you want to speed up deep learning—or even initiate it.

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Have Sound Fundamentals

Indeed, deep learning has already created its category under machine learning. However, like machine learning, deep understanding still requires developers and programmers to have sound fundamentals.

For example, you need to have a good mathematical foundation. Most of the algorithms and code you use will revolve around mathematical equations—primarily related to algebra and calculus. 

It will also help if you grasp statistics and probability theory well. After all, deep learning is mostly about those two things. With them, you’ll understand the right distribution of data to allow your AI to achieve the goal you want to teach it.  

Aside from that, it would be best if you grasped the most common programming languages used in machine learning, such as R, Perl, Ruby, and Python. With those languages, you need to know how to use them to analyze, extract, and process vast amounts of data.

Primarily, it’s best to focus on Python, as most deep learning setups use this programming language. You should mostly be familiar with how to construct lists, loops, invocations, conditional expressions, and definitions with it. 

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Of course, don’t forget about computer science fundamentals. Computer science mainly focuses on algorithms and computer architecture. Moreover, knowing computer science can make it easy for you to understand database systems, data structure, performance tuning, data visualization, object-oriented programming, and recursion.

Familiarize Yourself With Other Types Of Machine Learning

Currently, there are three types of machine learning: supervised, unsupervised, and reinforced learning. Most of these types can help you better understand what you’re going to do with your deep-learning algorithm. For example, most of the machine learning algorithms used to track user data in streaming sites and search engines may use one of these types.

Conclusion

Those are the things you need to know about deep learning. As you can see, getting into it requires a lot of commitment. Its demands are tremendous, and you can say a regular developer would need to invest a lot of money and time in it. So, before you even try it, be sure you can keep up with all the caveats and demands of deep learning.

Author

Ruby has been a writer and author for a while, and her content appears all across the tech world, from within ReadWrite, BusinessMagazine, ThriveGlobal, etc.

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