What is deep learning?

At a glance

• Specialized machine learning method based on deep neural networks.
• Uses mathematical models to imitate how the human brain works.
• Independently identifies patterns in large, unstructured datasets.
• Drives innovation in image recognition, language processing, and autonomous systems.
• Improves forecasts and data-driven decision-making in businesses.

Deep learning definition

Deep learning (DL) is a specialized method of machine learning (ML) that uses artificial neural networks to analyze and process complex patterns and structures in large datasets. Unlike traditional machine learning models, which often use shallow networks, deep learning models rely on deep networks with multiple layers, making them particularly powerful for identifying patterns in large and unstructured datasets.

By using multilayer neural networks, deep learning technology uses mathematical models to mimic how the human brain works, enabling machines to learn from experience and make decisions. Despite its high computational demands, deep learning offers significant advantages through the automation of complex tasks in areas such as image recognition, language processing, and autonomous systems. Businesses benefit from deep learning because it enables them to make more accurate predictions and use their data more intelligently.

How does deep learning work?

Deep learning enables machines to learn independently from large datasets and recognize complex patterns without requiring explicit programming. But how exactly does deep learning learn? Its functionality is largely based on the following components:

• Neural networks as the foundation: Deep learning is based on artificial neural networks, algorithms that mathematically model the human brain. These networks consist of multiple layers of neurons (nodes) connected to one another.
• Layered architecture: The networks have a deep architecture consisting of many layers (input layer, hidden layers, and output layer). Each layer further processes the output of the previous layer to extract increasingly detailed information.
• Forward propagation: Data flows through the neural network layer by layer, moving from the input layer to the output layer. At each layer, the network processes the data and passes it on to the next layer until a prediction (result) is produced. This process is known as forward propagation.
• Learning process (training): The network learns by comparing its predictions based on the input data with the actual results. This is done using a loss function that measures the error.
• Backpropagation: The network compares its prediction with the actual result and calculates the error — in other words, how far off it was. During backpropagation, this error is propagated backward through the network to adjust the connections (weights) between neurons. The goal is to make predictions more accurate on the next pass.
• Weights and bias: Each connection between neurons has a weight that determines the influence one neuron has on the next. The weights are continuously adjusted to improve the model’s accuracy.
• Use of machine learning frameworks: Frameworks such as TensorFlow, PyTorch, and JAX are commonly used to develop and train deep learning models.
• Computationally intensive processes: Deep learning requires substantial computing resources, often using GPUs or cloud computing to process large amounts of data efficiently.

What are deep learning algorithms, and which types are there?

Deep learning algorithms are specialized methods capable of identifying complex patterns and structures in data. Some of the most important types of deep learning algorithms include:

• Convolutional Neural Networks (CNNs) are particularly effective for processing image and video data. They use convolution operations to identify local patterns in data, such as those used in facial or object recognition.
• Recurrent Neural Networks (RNNs) are specialized in sequential data, such as time series or text. They retain information from previous inputs, making them ideal for applications such as machine translation and speech recognition.
• Long Short-Term Memory (LSTM) is a specialized type of RNN that processes long-term dependencies in data. LSTMs are particularly useful for tasks such as language modeling and text generation.
• Generative Adversarial Networks (GANs) consist of two networks — a generator and a discriminator — that are trained against each other. They are frequently used to generate realistic images or videos.
• Autoencoders learn to encode data into a compressed form and reconstruct it. They are often used for data compression or noise reduction.
• Deep Belief Networks (DBNs) are multilayer networks that are trained in an unsupervised manner to extract complex features from data. They are used in image and language processing.

Are neural networks deep learning? – Neural networks vs. deep learning

Deep learning and neural networks are closely connected because deep neural networks form the “core” of deep learning. They are inspired by the structure and functionality of the human brain and consist of multiple layers of artificial neurons. Each layer processes the input data in a different way and passes the information on to the next layer. Through this multistage process, neural networks can recognize highly complex patterns and generate highly accurate predictions.

Neural networks are therefore a fundamental concept in deep learning. While simple neural networks consist of only a few layers, deep learning models are characterized by many hidden layers — hence the term “deep neural networks.” This depth enables them to identify more abstract and complex relationships in data. Neural networks and deep learning are therefore inseparably linked, but simple neural networks with only a few layers are not necessarily considered deep learning.

What is the difference between deep learning and machine learning?

Deep learning and machine learning are both subsets of artificial intelligence (AI), but machine learning is not the same as deep learning. They differ in their complexity and how they work. While machine learning is well suited to smaller, structured datasets, deep learning excels with large amounts of data and complex tasks.

Machine learning encompasses algorithms that enable computers to learn from data and make predictions or decisions without being explicitly programmed. It also typically involves manual processes in which people determine which features in the data are important.

Deep learning, on the other hand, is a specialized form of machine learning based on deep neural networks. Unlike conventional machine learning models, deep learning automates these manual processes and can handle much more complex data structures. This also enables an end-to-end learning process — from data processing to output — in which the software independently learns how to solve a given task.

Application examples: where is deep learning used?

Deep learning delivers its benefits wherever large, often unstructured volumes of data are analyzed automatically. The following areas show typical use cases in companies.

Summary

Deep learning is a powerful machine learning method based on deep neural networks that enables machines to independently identify complex patterns in large datasets. In a business context, deep learning offers significant potential—from more accurate forecasts and anomaly detection to the automated analysis of unstructured data. Bissantz integrates deep learning methods into DeltaMaster so that controlling and management teams can benefit directly from powerful models—without technical barriers and with a clear focus on business-oriented decision support.