What Are Deep Learning Models? Types, Uses, and More

Deep learning is a subset of machine learning and artificial intelligence (AI) that mimics how a human brain functions, empowering computers to address complex patterns that create new insights and solutions. If you’ve used technology like a digital assistant on your phone, received a text alerting you of credit card fraud, or ridden in a self-driving car, you’ve likely used deep learning technologies.

A deep learning model is a compilation of nodes that connect and layer in neural networks, much like the human brain. These networks pass information through each layer, sending and receiving data to identity patterns. Deep learning models use different types of neural networks to achieve specific solutions. 

Read more to learn about the different types of deep learning models, how to train deep learning models, and what careers exist in the deep learning field.

What are deep learning models?

Deep learning models are complex networks that learn independently without human intervention. They apply deep learning algorithms to immense data sets to find patterns and solutions within the information. Typically, the models have three or more layers of neural networks to help process data. These models can process unstructured or unlabeled data, creating their own methods for identifying and understanding the information without a person telling the computer what to look for or solve.

Because deep learning models can identify both low- and high-dimensional data, they can take difficult-to-understand data sets and create simpler, more efficient categories. This ability allows the deep learning model to grow more accurate over time.

Read more: What Is Deep Learning? Definition, Examples, and Careers

Types of deep learning models

Deep learning systems use a variety of constructions and frameworks to achieve specific tasks and goals. Some types of deep learning models include:

• Convolutional neural networks: You can use convolutional neural networks for image processing and recognition.

• Recurrent neural networks: You can use recurrent neural networks for speech recognition and natural language processing.

• Long short-term memory networks: You can use long short-term memory networks for sequential prediction tasks, such as language modeling.

Learn more about shallow versus deep neural networks in this lecture from IBM's Introduction Deep Learning & Neural Networks course:

What are deep learning models used for?

You can use deep learning models in a wide range of fields, such as manufacturing, aerospace, health care, and electronics, to support the functions and goals of the professionals implementing deep learning techniques. 

These tasks tend to fall into four categories, which include:

• Computer vision: This is a computer’s ability to understand and process images, which is often used for content moderation, medical image analysis, facial recognition, and image classification.

• Speech recognition: This involves a computer’s ability to analyze and understand human speech. Speech recognition is primarily used for virtual assistants, such as Siri, which understand what you ask and provide answers.

• Recommendation engine: This is a computer’s ability to track and analyze a user’s habits to create tailored recommendations. This is for features like Netflix’s movie recommendation stream or content in your social media feeds.

• Natural language processing: This is a computer’s ability to understand text copy. You can use natural language processing for translation services, chatbots, and keyword indexing.

Read more: What Are Deep Learning Algorithms?

How do deep learning models work?

Deep learning models work by interacting with immense data sets and extracting patterns and solutions from them through learning styles similar to what humans naturally do. They use artificial neural networks (ANNs) to parse and process data sets. The networks operate using algorithms, which allow the computer to adapt, learn, and perform complex tasks on its own without needing a human to guide the learning.

Each type of deep learning model has different uses, but they all share the same learning and training process. To train a deep learning model, huge data sets need to feed into the network. This information passes from neuron to neuron, allowing the computer to analyze and understand the data as it moves through the network.

Who uses deep learning models?

Professionals who want to achieve specific goals and processes within their industry use deep learning models. Some of these professionals include data scientists or data engineers. Companies that create or use self-driving cars, factories, medical imaging systems, and defense systems are examples of industries that use deep learning models. It can also be found in many organizations working with automation and intelligence systems. 

Pros and cons of using deep learning models

Deep learning models come with many different pros and cons. Some benefits of deep learning models include:

• Its ability to analyze and process immense sets of unlabeled, unstructured data, often too complex and unwieldy for humans to process on their own

• It can learn information they aren’t specifically trained on, such as recommending new media based on your viewing habits compared to other users.

• Deep learning models are scalable and fast, so they have the ability to handle whatever data sets you might want to be processed without needing a lot of setup or maintenance.

Some limitations to consider before using deep learning models include:

• If the data fed into the model is too small, it may create false or inaccurate insights.

• The source of information or personal data might be a challenge if it infringes on privacy or security.

• Successful deep learning models require complex infrastructure and intensive computer setups to work.

How to get started in deep learning models

If you’re interested in getting started in deep learning models, there are numerous opportunities to learn more about this technology, as professionals with deep learning knowledge are in high demand. Data science boot camps are a great way to introduce yourself to deep learning model concepts. Free online courses, specializations, and Professional Certificates are also available for you to learn more before committing to formal training.

You can also seek a career in deep learning models. For example, you might want to become a machine learning engineer. These professionals are responsible for creating predictive and automated deep-learning models. They often work with other professionals, such as data scientists, to take data sets and feed them to the learning models. To become a machine learning engineer, you’ll want to have a bachelor’s degree in computer science and experience with deep learning models. A machine learning engineer's average annual base salary in the US is $104,664 [1].

Deepen your deep learning skills on Coursera

Sharpen your deep learning models knowledge and build a career in the field with courses and degrees on Coursera. With options such as DeepLearning.AI’s Deep Learning Specialization course, you’ll learn about the foundations of deep learning and the skills necessary to pursue a career in machine learning and artificial intelligence.

In DeepLearning.AI's TensorFlow Developer Professional Certificate, you'll explore how to build, train, and optimize deep neural networks and dive deep into computer vision, natural language processing, and time series analysis.