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Deep learning is a subfield of machine learning and thus also of artificial intelligence (AI). It involves the use of artificial neural networks that are designed to process information in a way that resembles the human brain.
What is deep learning based on?
- Neural networks: The basis of deep learning are artificial neural networks, which consist of layers of interconnected “neurons”. These neurons simulate biological brain cells and can process data using so-called “weights” and “activation functions”.
- Deep structures: The term “deep” refers to the many layers used in the network. Such deep networks can recognize complex patterns in data.
- Learning from data: Deep learning models learn from examples. They analyze large amounts of data and adjust their internal parameters to recognize patterns, correlations and features.
How does deep learning work?
Deep learning works by mimicking the way the human brain works, in particular through artificial neural networks, which consist of multiple layers of “neurons”. The processes of deep learning can be broken down into the following steps:
- Building a neural network
- Input layer: Takes in the raw data (e.g., an image, text or audio).
- Hidden layers: These layers process the data and extract features. The more layers the network has, the “deeper” it is considered to be. Each layer consists of neurons that are connected to the neurons of the next layer.
- Output layer: Provides the final result, e.g., a classification, prediction or decision.
- Processing the data
The data flows through the network in the form of numerical values (e.g., pixel values of an image or vectors for text):
- Weights and biases: Each connection between neurons has a weight that controls the strength of the signal it transmits. A bias is added to increase the flexibility of the model.
- Activation function: After calculating the weight, an activation function is applied to decide whether the signal is passed on.
- Training the network
The network is trained to learn from examples:
- Forward propagation: The input data is passed through the network and an initial prediction is calculated.
- Cost function (loss function): The error between the prediction and the actual result is calculated using a cost function.
- Backpropagation: The error is propagated backwards through the network to adjust the weights and biases.
- Optimization algorithm: An optimizer such as Stochastic Gradient Descent (SGD) adjusts the parameters to minimize the error.
- Repeat until convergence
This cycle (forward pass, error calculation, backward pass) is repeated until the error is minimal or the desired accuracy is achieved.
- Test and validate
After training, the model is tested with new, unseen data to ensure that it has generalized and not just memorized the training data (overfitting).
Areas of application for deep learning
Deep learning has become an important technology in a wide range of industries. It has enabled revolutionary advances, particularly in the field of image and video processing. With deep learning algorithms, computers can analyze images and videos with impressive precision. Applications such as object recognition, facial recognition in smartphones, or medical image diagnostics, where, for example, tumors are identified in X-ray images, show the extent of this technology. Even autonomous vehicles use deep learning to recognize traffic signs, pedestrians or other vehicles and make real-time decisions based on this information.
Another field that benefits greatly from deep learning is speech and text processing. Voice assistants such as Siri, Alexa or Google Assistant owe their ability to understand human language and respond meaningfully to this technology. Machine translation services such as Google Translate also use deep learning models to quickly and accurately translate texts between different languages. In the area of text generation and speech recognition, deep learning ensures that machines can create realistic and contextual texts or convert spoken language into text without errors.
Deep learning has also made many advances in healthcare. From the early detection of diseases such as cancer to the analysis of genetic data and the development of new drugs, the technology enables diagnoses, therapies and research results that were previously unthinkable. It enables doctors and researchers to better address the individual needs of patients and create personalized treatment plans.
The energy and environmental sectors also make extensive use of deep learning. For example, the analysis of satellite data is used to monitor environmental changes such as deforestation or climate change. In the energy industry, deep learning models help to predict energy consumption and optimize smart grids, which ultimately contributes to the conservation of resources.
E-commerce and streaming platforms also benefit from this technology. Recommendation systems that suggest personalized products, movies or music to customers are based on deep learning algorithms. Whether on Amazon, Netflix or Spotify, the technology ensures that users see content that perfectly matches their preferences.
Finally, deep learning is also becoming increasingly important in science and robotics. Astronomers use it to detect patterns in telescope data, while robots are becoming more autonomous and efficient thanks to their ability to analyze and navigate their environment.
What deep learning models are there?
There are several types of deep learning models designed for specific tasks and data types. These models use different architectures to learn complex patterns and relationships.
Convolutional Neural Networks (CNNs)
CNNs are the backbone of image processing and analysis. They can recognize features such as edges, shapes, and complex patterns in images and are therefore particularly powerful for visual data.
Areas of application:
- Image classification (e.g., facial recognition, object recognition)
- Image segmentation (e.g., medical image analysis)
- Applications in autonomous vehicles (e.g., recognition of traffic signs)
Recurrent Neural Networks (RNNs) and variants (e.g., LSTMs, GRUs)
RNNs and their variants are crucial for processing sequential data in which the context of previous data points is relevant. LSTMs (Long Short-Term Memory) and GRUs (Gated Recurrent Units) extend this capability by being able to process long-term dependencies more effectively.
Fields of application:
- Language processing (e.g., machine translation, text generation)
- Time series analysis (e.g., stock price forecasts)
- Speech recognition (e.g., Siri, Alexa)
Transformer models
Transformers have replaced RNNs in many areas, especially in language processing. They use attention mechanisms to process large amounts of data efficiently and can be parallelized, which speeds up training.
Fields of application:
- Text processing (e.g., sentiment analysis, machine translation)
- Text generation (e.g., GPT models like ChatGPT)
- Language comprehension (e.g., BERT for question-answering systems)
The pros and cons of deep learning
Deep learning offers numerous advantages that make it particularly attractive for complex tasks. A major plus point is the automatic feature recognition, which eliminates the need for time-consuming manual feature engineering. It can also achieve outstanding accuracy, especially with large, unstructured data sets such as images, texts or audio files. The versatility of deep learning enables applications in areas such as image processing, language processing or autonomous systems, and the models can be further improved with more data and computing power.
However, there are also disadvantages. Deep learning requires large amounts of data and powerful hardware such as GPUs, which results in high resource consumption. Training the models is also very time consuming. Furthermore, deep learning models work like a “black box”, which makes it difficult to understand their decisions. If the data basis is insufficient, there is also a risk of overfitting, which can limit the generalization ability.
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