Deep Learning: Neural Networks Push the Limits of AI

The world of machine learning has evolved at a rapid pace in recent years, and one technology in particular has attracted a lot of attention: Deep Learning,

From self-driving cars to intelligent voice assistants to image recognition, Deep Learning has pushed the boundaries of what is possible and enabled impressive advances in artificial intelligence.

But what is behind this fascinating concept? In this blog article, we take a look at the world of deep learning and how it can fundamentally help companies.

Read also our article on the Machine Learning. As recent research by J. Van Der Donckt et al. (2023) shows, Deep Learning models are sometimes disproportionate.


Van Der Donckt, J., Van Der Donckt, J., Deprost, E., Vandenbussche, N., Rademaker, M., Vandewiele, G., & Van Hoecke, S. (2023). Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring. Biomedical Signal Processing and Control, 81, 104429.

deep learning definition

Deep Learning: Definition

Deep Learning is a subarea of the machine learning, which uses artificial neural networks to recognize and understand complex patterns and structures in large amounts of data. 

It enables computers to automatically learn deep hierarchies of features and generate abstract representations. Deep Learning is based on multi-layer neural networks that pass information from one layer to the next. 

Training with sample data optimizes the weights of network connections to achieve good performance in classification, regression, or generation of new data. 

Deep Learning is used for a wide variety of applications, including. Image and speech recognition, natural language processing, autonomous vehicles and medical diagnostics. 

Doing so will enable great advances in AI research and machine learning, but it also brings challenges such as the need for large data sets, computationally intensive calculations, and interpretability of results.

Neural Networks and Deep Learning

Neural networks are models inspired by the biological nervous system and used to process information. They consist of a structure of artificial neurons arranged in layers that communicate with each other through connections.

The structure of a neural network consists of three main types of layers: 

  • Input Layer: The basis of the neural network is the input layer, which ensures that all necessary information is available. The input neurons have the important task of processing the received data and forwarding it to the next layer in a weighted manner.
  • Hidden layers (Hidden Layer): The layer that is located between the input and output layers is called the hidden layer. In contrast to the other two layers, which each consist of one layer, the hidden layer can consist of a large number of neuron layers. Here, the received information is re-weighted and passed from neuron to neuron until it reaches the output layer. The weighting is done at each level of the hidden layer, and the exact processing of the information is not visible. This is also the reason why it is called the "hidden layer". While the input and output layers reveal the incoming and outgoing data, the inner area of the neural network basically remains a "black box".
  • Output Layer: The last stage of the neural network is the output layer, which directly follows the last hidden layer. Here lie the output neurons, which contain the final decision as an information flow.

Function of the neuron in the network

A neuron in a neural network functions by combining inputs with certain weights and applying an activation function. Each neuron in a hidden layer or in the output layer receives inputs from the neurons in the previous layer and calculates a weighted sum of these inputs. The weights represent the strength of the connection between the neurons.

After the weighted sum is calculated, it is fed to an activation function that performs a nonlinear transformation. This activation function helps the network to capture complex nonlinear relationships in the data. Examples of activation functions include the sigmoid function, the Rectified Linear Unit (ReLU) function, and the tanh function.

The training process

The weights of the connections between neurons are optimized during the training process to achieve the desired performance of the network. 

This is where weight optimization algorithms such as Stochastic Gradient Descent (SGD) come into play. 

The SGD algorithm gradually adjusts the weights based on the calculated errors between the actual and desired outputs.

By training with large data sets, the neural network learns to adjust the weights to recognize patterns and features in the data and make useful predictions or classifications.

These fundamentals of structure, functioning of neurons, connections between layers, activation functions, and weight optimization algorithms form the basis for understanding and applying neural networks in various areas of machine learning, including Deep Learning.

deep learning architecture

Deep Learning Architectures

Deep learning architectures are specialized network structures capable of recognizing and learning complex patterns and features in large data sets. 

These are some of the commonly used deep learning architectures:

Convolutional Neural Networks (CNNs)

CNNs are particularly well suited for processing images and visual data. They use special layers such as Convolutional Layers for feature extraction from images, Pooling Layers for dimensionality reduction and Fully Connected Layers for classification.

Recurrent Neural Networks (RNNs).

RNNs specialize in processing sequences in which the previous output of one neuron serves as the input to the next neuron. This allows RNNs to capture contextual information over time, making them useful for tasks such as machine translation, speech recognition, and time series analysis.

Long Short-Term Memory (LSTM)

LSTM is a special variant of RNNs designed to solve the problem of the gradient disappearing or exploding in traditional RNNs. LSTM uses a memory structure to capture longer dependencies in sequences, which makes it particularly effective for tasks such as speech generation and text prediction.

Generative Adversarial Networks (GANs).

GANs consist of two neural networks: the generator and the discriminator. The generator generates new data, while the discriminator tries to distinguish between real and generated data. These networks are trained against each other, producing high-quality generated data. GANs find application in image generation, text generation, and other creative applications.


The Transformer is an architecture specifically designed for processing sequences. It uses attention mechanisms to capture information from different positions in a sequence and to model complex relationships. Transformers are especially widely used in machine translation and natural language processing.

One of the most important scientific publications with the beautiful title, "Attention Is All You Need" describes the attention mechanisms comprehensively. You can find the PDF here.


Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, ., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30th ed.

These architectures are just a few examples of the variety of deep learning architectures that have been developed. Each architecture has its strengths and is specialized for certain tasks. 

In practice, combinations and variations of these architectures are often used to achieve optimal results for specific applications.

deep learning training

Deep learning model training

Training deep learning models involves the process of adjusting the weights and bias parameters of the network to achieve optimal performance on the task. 

Here are the basic steps of the training process:

Data preparation

First, the training data is prepared. This includes splitting the data into training and validation sets, normalizing the data to ensure consistent scaling, and performing data augmentation techniques where necessary to expand the data set and improve the robustness of the model.


The weights and bias parameters of the neural network are initialized randomly. This initialization sets the initial state of the network before training begins.

Forward propagation

In forward propagation, the training data is passed through the network, layer by layer, calculating the weighted sums and the activations of the neurons. At the end, an output is generated.

Error calculation

The error between the actual outputs of the network and the desired outputs (based on the training data) is calculated. Different error metrics can be used, such as Mean Squared Error or Cross-Entropy Loss, depending on the type of task.

Backward propagation

In this step, the error is propagated backwards through the network to calculate the contributions of each weight to the error reduction. This is done using the backpropagation algorithm. The derivatives of the error by the weights are calculated and used to update the weights.

Weight update

The weights and bias parameters are updated based on the calculated derivatives and using an optimization algorithm such as Stochastic Gradient Descent (SGD) or its variants. This step optimizes the weights to minimize the error and improve the performance of the model.


The steps of forward and backward propagation are repeated for several epochs or iterations to gradually improve the model. An epoch means that the entire training set has been run through the network once. This process continues until the model shows satisfactory performance on the validation data or a certain number of epochs is reached.

Validation and test

After training, the model is checked against the validation data to evaluate its performance and make adjustments as necessary. Finally, the model is evaluated with the test data to obtain an independent assessment of its performance.

This training process is performed iteratively, gradually improving the weights to fit the model to the given task. 

Training deep learning models often requires a significant amount of data, computational resources, and time to achieve optimal results.

Deep Learning Applications

There are a variety of applications for Deep Learning in different fields. the following are some well-known examples:

Image and object recognition

Deep Learning is widely used in image processing to detect, classify and localize objects in images. Applications range from face recognition and automatic vehicle detection to medical imaging.

Natural Language Processing (NLP)

Deep learning models are used to understand and generate speech. This includes machine translation, text classification, sentiment analysis, chatbots and speech recognition.

Autonomous vehicles

Deep Learning plays a crucial role in the development of autonomous vehicles. It enables the recognition of traffic signs, pedestrians, vehicles and other objects as well as decision making and vehicle control.

Medical diagnosis

Deep Learning is used to analyze medical images such as CT scans or MRI scans and detect diseases. It also supports the prediction of disease progression and the development of personalized treatment plans.

Recommendation systems

Platforms like Netflix, Amazon, and Spotify use Deep Learning to generate personalized recommendations for movies, products, and music based on user preferences.

Speech generation

Deep learning models can be used to generate human-like text, such as automatic subtitling, literary works, or articles.

Financial Analysis

Deep Learning is used to analyze complex financial data, make forecasts, detect fraud, and develop automated trading strategies.


Deep Learning enables robots to perceive their environment, learn and perform tasks. Applications include robotics in production, service robotics and assistance robots for people with disabilities.

These are just a few examples, and the applications of deep learning are diverse and expanding across many industries, including finance, retail, healthcare, security, marketing and more.

green background with konfuzio logo

With Deep Learning to efficient text processing thanks to Konfuzio

At Konfuzio Deep Learning finds application in their Text analysis and data extraction platform, Konfuzio AI. The platform uses neural networks and other deep learning techniques to process unstructured text data and extract relevant information. 

Here are some specific examples of how Deep Learning is applied at Konfuzio:

Text recognition and classification

Deep learning models are used to automatically recognize texts and classify them into different categories. This enables efficient organization and processing of large amounts of text data.

Entity extraction

Deep Learning can be used to extract relevant entities such as names, dates, addresses or product numbers from texts. The system learns to identify and precisely label these entities.

Information extraction

Deep Learning enables the extraction of specific information from texts, such as invoice data, contract terms, or product specifications. The system can identify the relevant information and store it in a structured manner in a database.

Text analysis and classification

Deep learning models enable detailed text analysis and classification of texts according to specific criteria. For example, the system can detect sentiments in customer reviews or identify potential risks in contracts.

The application of Deep Learning at Konfuzio offers a number of benefits:


By using Deep Learning, large volumes of unstructured text data can be efficiently processed and analyzed. Automated text analysis accelerates workflows and significantly reduces manual effort.


Deep learning models are able to recognize complex patterns in texts and provide precise results. This leads to higher accuracy in the extraction of information and the classification of texts.


Deep learning technology allows Konfuzio to customize its platform to meet the needs of organizations, regardless of the size or volume of text data. The solution can be easily scaled to handle large volumes of data.


Deep learning models are able to learn from experience and adapt to different types of text data. The system can be continuously optimized to meet specific requirements and produce accurate results.

By leveraging Deep Learning, Konfuzio enables your organization to effectively leverage your unstructured text data, extract information, and gain key insights. 

This leads to more efficient data processing, improved business processes and informed decisions.

Challenges and limitations

The application of Deep Learning brings several benefits as well as challenges.

Advantages of Deep LearningChallenges with Deep Learning
Pattern recognition ability: Deep learning models are able to recognize and learn complex patterns in large data sets. This allows them to provide powerful predictions and classifications in areas such as image recognition, speech processing, and data prediction.Data quality and quantity: Deep learning models require a sufficient amount of high-quality training data to produce good results. Collecting, labeling, and cleaning large datasets can be time-consuming and costly.
Automated feature extraction: Deep learning models can automatically extract relevant features from data without the need for a manual feature extraction process. This enables efficient processing of large amounts of data and saves time and resources.Computing power and resources: Training deep learning models often requires significant computing power and specialized hardware such as GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units). Access to such resources can be expensive and limited.

Other advantages and disadvantages

Scalability: Deep learning models can be easily scaled to large data sets. By using parallel processing and specialized hardware such as GPUs, deep learning models can be effectively deployed even in large enterprises and organizations.Model complexity and hyperparameter tuning: Selecting the right architecture and hyperparameters for a deep learning model is a challenging task. Finding the optimal combination can be time consuming and often requires extensive experimentation and testing.
Adaptability: Deep learning models can adapt to new data and continuously improve their performance. By updating and re-training the model with new data, they can respond to changing requirements and environments.Overfitting: Deep learning models can be prone to overfitting in that the model learns the training data too well and loses the ability to correctly generalize new data. This can lead to low performance on unknown data.
Versatility: Deep Learning finds application in a wide range of fields and tasks. It has been successfully used in image recognition, speech recognition, machine translation, medical diagnosis, financial analysis, and many other applications. The versatile nature of Deep Learning makes it a powerful technology with broad applicability.Interpretability and explainability: Deep learning models are often complex black-box models whose decision making is difficult to understand. Explaining predictions and understanding how the model arrives at its results can be challenging, especially in sensitive applications such as medicine or law.

Conclusion: Outlook for the future

Deep Learning has sparked a revolution in machine learning in recent years. The ability of neural networks to recognize and learn complex patterns has led to impressive advances in areas such as image and speech recognition, natural language processing, and autonomous systems. The applications of Deep Learning are diverse and have the potential to transform numerous industries.

Looking ahead, we can expect to see further advances in Deep Learning. The technology is expected to evolve to handle even more complex tasks and achieve even better performance. New architectures, algorithms and tools will be developed to further improve the efficiency and accuracy of Deep Learning models.

In addition, the collaboration between Deep Learning and other emerging technologies such as robotics, the Internet of Things (IoT), and AI-driven automation will lead to exciting innovations. The synergy of these technologies will open up new application areas and opportunities that were previously unthinkable.

It is clear that Deep Learning will play a critical role in the future of Machine Learning and Artificial Intelligence. Companies and researchers should recognize the potential and focus on developing and deploying Deep Learning techniques to take advantage of this powerful technology and further push the boundaries of what is possible.

Janina Horn Avatar

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