Deep learning – data processing with neural networks

Deep learning vs. machine learning - what's the difference?

Both machine learning (ML) and deep learning (DL) are based on algorithms that learn from data. The difference lies in the complexity and structure of the models:

• Machine learning mainly uses flatter models such as decision trees or linear regressions. These often require manual feature engineering, i.e. experts have to extract relevant features from the data.
• Deep learning, on the other hand, is based on neural networks with multiple layers ("deep neural networks"). These networks learn independently which features are important and can therefore recognize more complex patterns.

A simple example: While a classic ML model defines image features such as edges or shapes by hand, a DL model can extract these features independently from the raw data.

How does deep learning work?

The system is based on neural networks inspired by the structure of the human brain. These networks consist of "neurons" that are arranged in several layers:

• Input layer: Receives the data (e.g. image or text)
• Hidden layers: Perform calculations to recognize patterns
• Output layer: Delivers the result (e.g. classification or prediction)

Each connection between the neurons has a weight that is adjusted during training to improve accuracy.

Use of deep learning on mini PCs

Mini PCs can also run complex deep learning applications thanks to powerful hardware and optimized software. We have put together a few examples:

• Image and object recognition: With deep learning frameworks such as TensorFlow Lite or PyTorch, mini PCs can recognize faces or identify objects in real time. This is useful in surveillance systems or smart home devices, for example.
• Speech recognition: Projects such as Jasper or DeepSpeech make it possible to recognize speech on mini PCs and convert it into text. This technology is used in voice assistants.
• Predictive maintenance: In an industrial environment, mini PCs can analyze data from sensors and detect anomalies in order to predict maintenance requirements.

Deep learning on mini PCs offers numerous advantages, but also presents some challenges. The compact design of the mini PCs is an advantage, and the little helpers are also more energy and cost efficient than traditional servers.

However, there are challenges in terms of limited computing power and memory. However, special hardware such as AI accelerators (e.g. NIVIDIA Jetson Nano) can partially overcome these limitations.

Deep learning with the NINETEEN Q670E

Deep learning with spo-comm Mini-PCs

Our product portfolio also includes the odd mini PC that masters deep learning with flying colors. For example, our NINETEEN Q670E, which should not be reduced solely to its 19-inch size, but also to what's inside it. The NINETEEN impresses with an Intel® Core™ i5-12500T CPU, up to 65GB RAM and optional NVIDIA RTX graphics card. It also offers some special features, such as a PCIe slot for an additional graphics card, RAID network with multiple SSDs, iAMT, Wake on LAN and much more!

Conclusion

Deep learning is revolutionizing numerous industries and is becoming increasingly accessible, even on mini PCs. From image and speech recognition to industrial applications - the possibilities are almost limitless.

Whether in research, industry or the smart home sector - mini PCs make deep learning more tangible and affordable. With the right combination of hardware and software, (almost) no AI application stands in the way!