Deep learning is one of the hottest up-and-coming job sectors in the world, with a market currently ranging between $3.5 and $5.8 trillion. On average, a Deep Learning Engineer earns $135,878 a year, but salaries can climb even higher.
The job prospects for Deep Learning Engineers are looking good as well, with a projected growth rate of 11% a year between now and 2029.
Deep learning, a subset of machine learning, plays a critical role in the way people gather and analyze data and how they use it to automate everything from statistical analysis to self-driving cars. Read on to learn what deep learning is, how it works, how it's used, and the programming languages you need for a career in deep learning.
The basics of deep learning
Deep learning is a kind of machine learning where a computer analyzes algorithms and their results to "learn" ways of improving processes and creating new ones.
A deep learning system consists of a series of levels. Each level learns how to translate its input data into a composite representation that is slightly more abstract than before it reached that level.
Then, that information is passed to the next level, which does the same thing before passing it to the subsequent level, and so on. In this way, the system acquires an increasingly accurate representation of the data it's processing.
For example, a deep learning system designed for facial recognition may process an image of a face in this manner:
- Layer 1 may turn the overall shape of everything in the image of the face into pixels, assigning them to a grid.
- Layer 2 may group different sets of edges according to shapes. Instead of one grid of all the pixels in the image, there may be several smaller grids representing the contours of different elements of the face.
- Layer 3 may recognize that the shapes organized in Layer 2 correspond with a nose, eyes, and mouth.
- Layer 4 may then take the information from layer 3 and recognize that this image has a face in it.
The deep learning system can also decide which features belong at which level all by itself. Even though it can do a lot without human intervention, the system may need to be manually told how many layers it needs to have to attain a deep enough understanding of the data.
In the facial recognition example above, for instance, a few more layers would have to be added to enable the system to differentiate one person's face from another's. The computations of a deep learning system depend on artificial neural networks to function.
What are artificial neural networks?
Artificial neural networks consist of nodes that receive data from and send data to other nodes in the network. They're designed to imitate the neural network of the human brain.
Each node is programmed with a specific weight and threshold. If the output value of a node falls above the threshold, the node gets activated, and it then sends data to the following layer in the neural network.
In deep learning, a neural network is used to discover patterns, more efficient ways of doing things, and new processes designed to accomplish specific goals.
How deep learning works
Deep learning hinges on programming a neural network to learn.
Each node in the neural network activates according to programming rules, and the deep learning model studies how and when the nodes get activated. It then learns about the process and can provide insights into how it can be improved.
Here's a simple example of how deep learning can use a neural network to teach itself how to improve a process:
Suppose you have a machine in a factory with a few moving parts that could pose a safety hazard if touched by a human or their clothing. Proximity sensors are set up near the machine, as well as at specific distances away from it. Each proximity sensor indicates when someone has crossed that specific distance from the machine, and their data is fed into a deep learning system.
The nodes in the deep learning model collect information regarding where someone goes after passing each proximity sensor. It also calculates the likelihood of someone going to the next sensor after crossing the one before it.
It could output data such as "There's an 85% chance that someone 12 feet away from the machine will eventually get 3 feet away from it as they travel between sensors A, B, C, and D."
The deep learning model can then use the data it collects to learn:
- When people should be warned about getting too close to the machine
- When to automatically shut the machine off if someone gets too close
- The safest way to pass by the machine without getting too close or unnecessarily inhibiting your movement through the factory
With this data, the system can help employees move around safely while still traveling freely enough to get things done quickly. It can even be used to project the optimal path of travel on the factory floor or guide automated vehicles as they transport people by the machine.
All of this learning hinges on how the nodes in the neural network process data.
Deep learning vs. other types of machine learning
As we explained earlier, deep learning is a subset of machine learning. In other words, all deep learning is machine learning, but not all machine learning is deep learning. Other types of machine learning are often more basic and simple than deep learning.
For example, basic machine learning can be used to examine a database that holds information about sales revenue, month-by-month marketing costs, when sales associates work, and the times of day sales are made.
It can then tell a Sales Manager who the most effective sales reps are on Friday afternoons in August and how to adjust their compensation to reward outstanding performance.
Deep learning, on the other hand, can figure out more complex problems. For example, deep learning is used to provide vision to self-driving vehicles. The deep learning system can collect data regarding:
- Objects surrounding the vehicle
- How fast objects are moving in relation to the vehicle
- The direction of travel of moving objects
It then uses this data to make decisions like:
- How hard and when to apply the brake if there's a danger of collision
- How to differentiate objects from each other, such as a ball from a dog or human
- How to adjust the steering of a vehicle given the speed and trajectory of another vehicle alongside it
How deep learning is used
Along with industrial automation and automated driving technologies, deep learning is used in:
- Defense systems. It can identify objects and areas of interest, ensuring it's safe for troops to land in a specific spot.
- Medical research. Deep learning can automatically differentiate cancer cells from healthy cells.
- Automatic speech. You can use deep learning to listen to and study speech, as well as generate more human-like speech patterns.
- Cybersecurity. With deep learning, computer software can detect cyber threats based on their behavior within a network.
- Image recognition. Deep learning can be used to recognize the kinds of images presented to the system, such as people's faces, vehicles, machinery, and the vehicles or weapons of enemy combatants.
- Text generation. A deep learning model can learn the grammar and punctuation rules of a language and generate text that reads like a human wrote it.
- Adding color to pictures and videos. Before deep learning, people had to add color to black and white images manually. Now, deep learning systems can automatically add a full-color spectrum.
Programming languages for deep learning
Deep learning systems can be programmed with several different languages. Sometimes, the language chosen may depend more on the computer or system processing it than the problem the deep learning system is trying to solve, especially because algorithms can be written in a variety of languages.
We can teach you some of the most popular deep learning languages, including:
With our courses, you'll gain a solid foundation in the languages used to write deep learning programs, such as those above. You can also take advantage of specific Skill and Career Paths that incorporate deep learning, such as:
We'll also help you develop an extensive portfolio of your work while you learn, making it easier to land a job as a deep learning professional. Get started today!