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.
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 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.
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.
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:
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.
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
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.
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.
Deep learning can be considered a subset of machine learning but with a different approach to how the actual learning goes on. Traditional ML systems depend on algorithms that learn from data using manually selected features. Specific inputs such as product specs or logistics data can be entered. The model then uses one or more algorithms to find patterns and make predictions. This method works well when data is structured and clear.
By contrast, deep learning models learn directly from raw data. When they are trained, instead of relying on predefined inputs, they use layered neural networks to automatically extract the most relevant features. These networks adjust themselves over time, identifying patterns that might be too subtle or complex for a human to define.
Because of this, deep learning is especially powerful when working with high-volume, unstructured data like images or audio. It powers things like computer vision or complex natural language processing without needing each feature to be hand-engineered.
In practice, traditional ML is often the better choice when the problem is linear, well understood, and transparency matters. Deep learning is better suited when the data is too complex for manual analysis, and the task at hand requires flexibility, nuance, and scale. That said, in today’s cloud-connected enterprise systems, the two approaches are typically used together. ML models handle structured insights, and deep learning powers richer and adaptive experiences.
Machine learning models often rely on manual feature selection or a lot of pre-processing of data to make it digestible. Deep learning models, however, can automatically discover patterns through multiple layers of abstraction. This gives them the ability to make increasingly accurate decisions and makes them wellsuited to highly complex tasks.
Traditional business data was often linear and numerical. But these days, some of your most valuable intel can come from customer videos, conversation transcripts, IoT sensors, and a rapidly increasing range of data-generating touchpoints. Deep learning excels at analyzing and making sense of disparate datasets.
Data volumes and computing power go hand in hand. As business systems become faster and more powerful, they capture and manage staggering amounts of information. Many traditional ML-powered systems have plateaus and limits that are a fraction of what modern deep learning systems can easily handle.
Once trained to recognize, say, defects in a car’s braking system, deep learning-driven solutions are able to extrapolate that knowledge. They could apply those learnings and training protocols to teach themselves how to recognize defects in another part of a vehicle, if given data from that manufacturing process.
Deep models learn features at different levels of abstraction. For example, a model might first detect sensor fluctuations, then specific wear patterns, then finally, learn to predict equipment failure. This layered understanding is valuable in fields where minor differences can have major implications.
Deep learning can drive applications that need to process and respond to data in real time, such as anomaly detection, or autonomous navigation. Depending on the case, these accurate observations can be programmed to automate rapid notification or action.
With the proliferation of data types and volumes comes the challenge of trying to manually locate, understand, and action a piece of information. This kind of processing is where deep learning comes into its own. It allows your teams to not only access and rapidly analyze data – but to also get reports and summaries that are presented in clear and understandable ways. Any operational task that requires simplification of complexity is one for which deep learning-powered solutions will probably be a good fit.
Deep learning has increasingly powerful applications across industries that rely on complex systems, detailed data, and continuous optimization. Below are few specific examples of how these technologies are being used to conquer challenges unique to these sectors:
Deep learning automates car part inspections during assembly and enables autonomous driving by interpreting road conditions, obstacles, and traffic patterns in real time. It also supports voice assistants and personalization by analyzing driver preferences and behavior.
Deep learning processes satellite imagery for surveillance, navigation, or mission planning. It’s also used in predictive maintenance, flagging subtle performance anomalies in aircraft or equipment before they lead to critical failures. This helps reduce downtime and increase safety.
In fashion, deep learning can help create new styles, suggest complementary pieces, and simulate how garments will fit across different body types. It also supports trend prediction by analyzing vast datasets from social media, e-commerce, and past sales.
In F&B, deep learning can monitor product quality through image recognition and detect defects or irregularities in prepared goods, produce, or packaging. It’s also being used to automate compliance tracking by analyzing sensor data in real time and triggering alerts when environmental thresholds are breached.
Deep learning supports advanced defect detection on production lines, especially for microscopic or irregular flaws that traditional vision systems might miss. It further enhances robotics by enabling more nuanced control and dynamic decision-making in automated processes.
While deep learning enables powerful breakthroughs in automation and pattern recognition, it comes with its own set of challenges – many of which are more pronounced than in traditional machine learning. These issues are important for business and technology leaders to understand when considering how and where to adopt deep learning.
Deep learning strengthens entire organizations by powering new ways to operate, compete, and grow. Its ability to extract meaning from broad data sources opens business-wide advantages that more basic automation simply can’t deliver.
Deep learning gives AI solutions the power to seem almost human. Not to mention the ability to perform remarkable and complex tasks with a level of speed and efficiency that we couldn’t have imagined, even just a few years ago. So, yes, the potential of deep learning to transform business operations is significant – even staggering. But at the end of the day, it’s a tool. And like any tool, it only functions properly when in the hands of a good practitioner. Can it replace humans in a lot of tasks? Yes. But so could every innovation from the steam engine to the assembly line. AI and deep learning come into their own when they are trained and used by skilled and able humans who come up with new and clever ways to leverage their functionality – rather than becoming overly dependent upon them.
Learn how Infor’s industry-specific AI solutions – powered by deep learning – can help you drive hyper-productivity and operate at a faster pace.