Deep learning is a type of machine learning (ML) used in many artificial intelligence (AI) applications. It is particularly effective for complex, multi-step tasks such as image recognition, natural language processing (NLP), speech recognition, and identifying patterns in large datasets.

Deep learning models are built using artificial neural networks (ANNs) — multilayered architectures inspired by the structure and function of the human brain — to support advanced pattern recognition and decision-making. To be considered a deep learning model, the network typically includes at least three layers: an input layer, one or more hidden layers, and an output layer. Each layer contains nodes, also known as neurons, that process and transmit information to achieve a specific outcome.

These models require significant computational resources and large volumes of data to train effectively. While labeled data, where inputs are tagged with the correct output, is often used, deep learning can also extract insights from unlabeled data, depending on the learning approach. 

Once trained, these models improve over time as they are exposed to more data, making them well-suited for dynamic environments such as fraud detection in finance, diagnostic imaging in healthcare, or demand forecasting in retail.

What are the different types of deep learning? 

There are several types of deep learning architectures, each designed for specific tasks and levels of complexity. Depending on the application, some are more commonly used than others.

Convolutional neural networks (CNNs)

Convolutional neural networks are especially effective for image-related tasks, such as unlocking a phone using facial recognition. CNNs excel at identifying objects even when images are partially obscured or low in quality.

They are widely used in computer vision, a field of AI that enables systems to interpret and respond to visual inputs such as images or video. In enterprise contexts, CNNs support use cases such as analyzing medical imaging in healthcare or monitoring shelf inventory in retail through camera feeds.

Deep reinforcement learning

Deep reinforcement learning trains models by enabling them to interact with an environment and learn through trial and error. The model receives feedback in the form of rewards or penalties based on its actions and uses this feedback to improve decision-making over time. 

This approach is used in recommendation systems — for example, a media platform suggesting content based on user engagement (positive feedback) or disinterest (negative feedback). In enterprise applications, reinforcement learning can optimize supply chains, personalize digital experiences, or automate trading strategies.

Recurrent neural networks (RNNs)

Recurrent neural networks are designed for sequential data information where the order of inputs matters. They are commonly applied in natural language processing (NLP) and speech recognition. 

RNNs have a memory component that allows them to consider the context of previous words or phrases, making them effective for tasks such as language translation, text generation, or real-time transcription. In enterprise settings, RNNs power applications such as customer support chatbots, voice assistants in healthcare, or compliance monitoring in finance.

How does deep learning work?

Deep learning uses neural networks to learn from data. These networks are composed of nodes (also called neurons), each of which is responsible for processing a part of the input. As data moves through the network, it is transformed at each layer. This process relies on weights — numerical values that represent the strength or importance of a connection — and biases, which help the model adjust its outputs. Together, these parameters enable the model to make increasingly accurate decisions, mimicking certain aspects of human learning.

There are three key stages involved in creating and using a deep learning model:

Development

As with other AI models, the development stage involves collecting and preparing data for training. The type of data, such as images, text, or audio, depends on the intended use case. For example, a financial institution may use transaction records, while a healthcare provider may work with medical imaging. The goal is to curate datasets that the model can learn from by identifying similarities, differences, and patterns relevant to its task.

Training

During training, the model learns to perform the task by adjusting internal parameters based on feedback. Data enters the network through the input layer and moves through successive hidden layers. For example, in an image recognition task, the first layer might detect basic edges, the next layer might recognize shapes, and a deeper layer might identify specific objects.

The model initially makes predictions and then compares them to the correct outcomes. If it gets the prediction wrong, it adjusts the weights and biases using a method called backpropagation, repeating this process across many iterations to improve accuracy. Over time, the model learns to generalize from training data and make better predictions on new inputs.

Inference

Once trained, the model is evaluated through inference — the stage where it applies its learned knowledge to new, real-world data. This step measures how accurately and efficiently the model generates outputs. For enterprise use cases, inference needs to balance speed and performance — for example, delivering real-time fraud alerts in finance or generating personalized product recommendations in retail.

If performance issues arise, adjustments may be made, such as pruning underperforming parts of the network or optimizing architecture for faster response times. This is similar to adjusting image resolution for different formats depending on performance and clarity requirements.

Deep learning use cases 

Deep learning powers many real-world applications, especially those that automate tasks traditionally requiring human intelligence, such as describing images, recognizing speech, or interpreting complex patterns in data.

  • Image recognition: Deep learning models can identify objects, features, and faces within images, even when visual input is unclear or varied. This is used in automatic facial recognition at airport passport gates, mobile device authentication, and quality control in retail logistics. Models learn to distinguish between visual elements in order to classify, describe, or trigger specific actions based on image content.
  • Natural language processing (NLP): NLP applications rely on deep learning for tasks like speech recognition, chatbot interactions, digital assistants, and real-time translation. These models help systems interpret spoken or written language and generate appropriate responses. Voice-activated technologies, such as smart home assistants or voice-controlled TV remotes, also use deep learning to understand and respond to user commands.
  • Data analysis: Deep learning excels at identifying patterns in large datasets and generating accurate predictions. In healthcare, it enhances diagnostic accuracy by analyzing medical images and flagging anomalies that may require further review. These capabilities help enterprises make faster, more informed decisions at scale.