What is deep learning?
Deep learning gives AI systems the ability to analyze and respond to unstructured data with incredible precision. Learn how the fast-evolving technology is driving real-world transformation across industries.
All deep learning is machine learning, but not all machine learning is deep. Machine learning is powered by one or more algorithms and gives systems the ability to analyze and predict based upon structured, historical data. Deep learning, on the other hand, goes a step further by taking that training data and passing it through a powerful neural network – detecting subtle patterns and relationships in data at levels of complexity that humans can’t easily see. It’s especially useful for working with unstructured data: text, images, video, sound, or sensor feeds. Deep learning models can automatically learn which features matter most, without being manually told what to look for. This makes them incredibly effective for tasks like language translation, visual recognition, or autonomous operations.
Deep learning definition
Deep learning is a type of machine learning that uses multi-layered neural networks to automatically learn patterns from large, unstructured datasets. It excels at tasks like image recognition, speech processing, and generative AI by learning complex features without human-defined rules.
Understanding deep learning: What are neural networks?
Deep learning (DL) systems are built on artificial neural networks. These mathematical structures are actually inspired by the architecture of the human brain. They are made up of layers of interconnected nodes, or “neurons,” each passing signals to the next – weighted as to their value. As data moves through these layers, the network adjusts those weights to minimize error, improving its ability to detect patterns and make predictions.
The learning is called “deep” because of the number of hidden layers between the input and output. This means that the data is transformed in increasingly abstract ways as it moves along. Early layers in a vision model might detect, say, edges and shapes. Later ones recognize objects or faces. In language models, they capture grammar, then meaning, and then tone.
Training a deep learning model involves feeding it large datasets, comparing its outputs to known answers, and updating weights using various techniques depending on the desired output. The more data and layers involved, the more nuanced and powerful the model can become.
Types of deep learning models
Deep learning includes different types of neural networks that are each suited to different kinds of input and tasks. They are set apart by how they handle the structure, sequence, or spatial relationships of data. This specialization is one of the things that makes deep learning models especially powerful for high-dimensional or unstructured data. Below are some of the more common deep learning models:
Feedforward neural networks (FNNs)
A feedforward neural network is the simplest model, moving data in one direction from input to output. It is primarily used for basic classification or regression tasks that predict specific numerical values.
Convolutional neural networks (CNNs)
Designed to process complex data such as images or videos, these models use filters to scan for spatial patterns like edges, textures, or objects. This makes them foundational in things like computer vision, medical imaging, and defect detection
Recurrent neural networks (RNNs)
RNNs are ideally suited for sequential data like time series, speech, or text. They include loops that help retain the memory of previous inputs. Variants include things like long short-term memory (LSTM) networks which allow them to reference prolonged chains of data.
Transformer-based models
These models are now dominant in large language models (LLMs) and natural language processing (NLP). They can handle large chunks of sequential data all at once, rather than item by item. They weigh relationships between words or elements for better outputs and accuracy.