Deep learning, a field within AI, is a type of machine learning whereby software uses deep layers of neural networks to find patterns in complex data. It’s a highly specialized but burgeoning field—and an attractive one for tech professionals who want to participate in the growing AI revolution.
One important part of deep learning is what’s known as “feature learning,” whereby the software is able to determine what aspects of complex data are more important than others, and then use that knowledge to recognize patterns. With traditional machine learning applied to facial recognition, software might measure the ratios of the different distances between facial features such as eyes, noses, ears, and mouths. But with deep learning, the software isn’t programmed specifically to make such measurements; instead, the deep learning application might be given many different photos of the same person, and then start to discover on its own what similarities to focus on.
As you can imagine, deep learning is a highly advanced profession and requires a great deal of study. Let’s look at how you can specialize in deep learning.
Start with Machine Learning and Mathematics
Deep learning is a specialization of the machine learning field. That means you need to learn as much as you can about machine learning, starting with a strong understanding of advanced mathematics:
- Linear algebra. This is the mathematics of vectors in multiple dimensions.
- Calculus. The mathematical study of continuous change.
- Probability and Statistics. Probability is the study of chance, while statistics is analyzing data.
Learn Programming
In order to specialize in deep learning, you’ll need to make sure you know several programming languages, such as Java and C++, and especially Python. Regarding Python, you’ll need to know various libraries including:
- Numpy: This is the standard numerical package for Python. Although not officially part of Python, it’s one of the most-used packages. It allows the easy creation of multidimensional arrays and matrices, including mathematical operations for arrays and matrices.
- Pandas: This is one of the most popular libraries for data analysis. It works closely with numpy.
- Matplotlib: This library, which fits with both numpy and pandas, provides data visualization.
Additionally, you’ll need to learn machine learning frameworks including:
- TensorFlow: This is a machine learning system created by Google.
- Keras: This is a neural network framework for Python.
- Torch and PyTorch: Torch is an open-source machine learning library, and PyTorch is a Python interface for Torch.
- MXNet: This is an open-source framework for deep learning. It an Apache Foundation project.
Learn Cloud Concepts
Deep learning software needs to run on computers that have a lot of cores and a lot of memory. While you can certainly purchase such computers, more likely you’ll want to spin up such computing in the cloud. That means understanding such programming concepts as:
- Multicore and multithreaded programming
- Asynchronous programming
- High-core GPU programming (such as NVIDIA’s Cuda)
You’ll be doing such programming in the cloud, which means you’ll need to learn technologies such as:
- Spinning up multi-core servers in AWS and other clouds
- Containerization
- Lambda functions in AWS
Deep Learning Concepts
In addition to the above, you’ll need to learn the concepts of deep learning itself. Below we list some concepts that, for many of us, are going to look incredibly foreign. That aside, these are the names of concepts you’ll need to study:
- Neural Networks including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Neural networks are modeled after the neurons in a human brain. They are computer software and libraries that recognize patterns and gradually learn over time. CNNs analyze visual data by recognizing textures and shapes. RNNs on the other hand learn from sequential data and finding patterns inside them.
- Autoencoders are another type of neural network that find ways to compress data and remove noise from it.
- Generative Adversarial Networks (GANs). This is an especially complicated topic, but think of it as two pieces of software competing against each other, with the system learning as it does so. It’s used, for example, to create realistic-looking photos of people who don’t actually exist.
- Transformers. This is a part of today’s generative AI whereby the code will understand context and relationship between words in text. (We see this one at use in tools like ChatGPT).
- Natural Language Processing. This is related to transformers and it allows us to build tools that can process human language, again like ChatGPT is able to do.
- Model Training. Machine learning and deep learning make use of data (called a model) that need to be trained with existing data. For example, we know that ChatGPT was fed millions of documents that it “read” and “learned.”
- Model Evaluation and Validation. This involves determining how well a model is trained.
- Reinforcement Learning. This is a type of learning whereby the system is rewarded when it reaches a correct conclusion.
This list in particular might seem a bit intimidating. Look up examples online where these concepts are actually being used within Python code, and that might help you put this knowledge in vital context.
Get a Degree and Take Courses
To land a job as a deep learning specialist, you’ll almost certainly need a degree. You’ll typically first need a bachelor’s degree in computer science; after that, you’ll likely go on to get a Master’s degree in a program such as machine learning or artificial intelligence. Should you get a PhD? Not necessarily—unless you want to go into research (which is a huge decision).
Note, however, that if you presently have a bachelor’s degree in computer science, you might be able to take online non-degree courses and study as much as you can in the field, and work on actual open-source projects on GitHub and other repos. These projects would be ideal places to help you learn and refine your skills. And because deep learning is a subset of machine learning, you can look for similar projects in that topic here.
This might not be enough to land a job without a Master’s degree, but it might at least help you determine whether deep learning is a right fit for you.
What Does a Deep Learning Career Path Look Like?
Deep Learning is a specialty within a machine learning career. That means you’ll start out as either a software developer or data analyst, and then move up to a data scientist role, as that’s the starting path for a machine learning engineer.
The next step is potentially tricky. You might not work at a company that does deep learning. This is why you’ll need to learn as much as you can until you make potentially a lateral move to another company that has the necessary resources to pursue deep learning as part of its broader strategy. After that, depending on the different titles within the organization, you might move up to a senior deep learning specialist.
From there, it depends on whether you want to move into management. Such management jobs tend to be less technical in nature, instead relying on your technical knowledge to make important business decisions. You could land titles such as Director of Machine Learning, or Director of AI, or even Chief Technical Officer.
However, an alternate path after becoming a deep learning specialist is to go into research (which may indeed require a PhD). In the years to come, there will be more and more AI companies, and they’ll need researchers who know the field inside and out.
Conclusion
Deep learning is a highly specialized role, but more and more jobs are opening up in the field. AI companies are going to be looking for experts in this field for the foreseeable future. This is a huge opportunity. If you’re determined and willing to put in the effort to master this specialty, you’re likely to have an exciting, thriving career.
