Machine learning, a subset of artificial intelligence, has its own subset, deep learning, to help it match and exceed human abilities through enhanced compute power.
In this blog, you’ll learn more about deep learning (DL) and how it uses data input to solve complex problems and deliver actionable insights in real-time.
What is deep learning (DL)?
A subset of machine learning, deep learning (DL) trains computers to perform human tasks like speech recognition, image identification, and prediction making.
It improves the ability to classify, recognize, detect, and describe using data input.
There has been an increased interest in DL as artificial intelligence (AI) has risen in popularity.
Why is DL important?
Deep learning algorithms can process large numbers of features, making them very powerful when dealing with unstructured data.
A lot of computational power is needed to solve DL problems, as algorithms possess many layers, and large volumes of data are needed to train the networks.
When dealing with vast amounts of data, these algorithms can be very helpful for solving complex problems.
Additionally, DL methods can continuously improve and adapt to changes in underlying information patterns, introducing more dynamic behavior into data analytics.
How does DL work?
With DL, data trains the computer through algorithms to learn on its own by recognizing patterns using layers of processing.
DL changes the way people think about representing the problems you are solving with analytics. Instead of telling a computer how to solve a problem, it trains the computer to solve the problem itself.
Here are three primary features of deep learning:
- Feature representation: DL is a paradigm shift in the model building that moves from feature engineering to feature representation.
- Deep learning layers: Instead of using known variables to predict unknowns, DL looks at layers of data to recognize latent features.
- Deep learning results: The promise of DL is that it can lead to predictive systems that generalize well, adapt well, and continuously improve as data arrives.
What supports DL?
There are several developments that have helped to advance deep learning:
- Analytical improvements have boosted the performance of DL methods.
- New machine learning approaches have improved the accuracy of DL models.
- New classes of neural networks have been developed that fit well for applications like text translation and image classification.
- A lot more data is available to build neural networks with many deep layers.
- Computational advances of distributed cloud computing and graphical processing units have put incredible computing power at users’ disposal.
What are the downsides of DL?
There are, however, some drawbacks to deep learning:
- It requires large amounts of data in order to perform well.
- DL algorithms are extremely expensive to train due to complex data models. Additionally, it requires expensive GPUs and hundreds of machines.
- It is not easy to comprehend output and requires classifiers to do so. (This is where neural networks play a huge role.)
At its core, deep learning empowers high-performance compute solutions to deliver actionable insights in real-time through recognition, identification, and prediction-making capabilities.
With increased computational power, DL helps to increase situational awareness and strengthen knowledge of adversary and environment, shortening the time between detection and response.
From the control room to the battlefield, deep learning enables our warfighters with the critical data necessary to detect, track, and engage with enemy threats.