Deep Learning has become a core technology in machine learning, utilized by companies such as Google, Facebook, and Microsoft to solve complex machine learning and AI problems.
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Only a few topics have caused more buzz in the machine learning field recently than deep learning. Deep learning encompasses various types of complex neural network architectures that are suitable for processing image, sound, and text information, among others. However, classical data science questions like classification or regression problems can also be solved with deep learning methods.
The models are made “deep” by modeling not just one layer of neurons, as with traditional neural networks, but several layers in the network architecture. This enables the neural network to derive complex connections, interactions, and abstractions from the data. However, training deep learning models requires a lot of experience and skill.
Our data scientists advise you on the development of deep learning applications and projects. We develop the required models in PyTorch and TensorFlow and implement the models in your IT environment. Take advantage of the exciting possibilities that deep learning offers for your business.
Use cases for
Deep learning is already widely used in identifying objects in image or video material.
Deep learning architectures enable the analysis, understanding, and generation of human language.
Many advanced translation solutions are based on complex deep learning models.
Generative AI aims to create structured data and content such as text, code, audio material, images, or videos.
Modern voice assistants like Siri and Alexa can only be implemented with the help of deep learning.
Deep learning methods are applied in complex prediction problems to achieve the desired outcome.
Many complex recommendation systems like those of Facebook, Netflix, and Instagram are based on complex neural networks.
Deep learning is crucial for autonomous driving, as many data points need to be processed quickly.
Benefits of Deep Learning
Processing large data sets
Deep learning can process large amounts of unstructured data such as image, text, or video material.
Due to the large number of parameters that a deep learning model possesses, better results can be achieved than with traditional methods.
Solving complex problems
Deep learning is particularly suitable for solving complex problems, as neural networks can be flexibly designed.
There are already various deep learning models that have been created in advance and can now be used directly or adapted to your own data.
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