Deep learning (Deep Learning): definition and issues

L’deep learning (Deep Learning) is a sub-branch of Machine learning which is based on artificial neural networks made up of several layers (Deep Neural Networks). It allows machines to learn from large amounts of data By capturing complex patterns without explicit human intervention.

Why is deep learning crucial?

Deep Learning is the basis of the most spectacular advances in artificial intelligence and feeds many applications:

• Image and videos recognition (e.g. medical diagnosis, computer vision, security analysis)
• Natural language treatment (NLP) (e.g. chatgpt, automatic translation, feeling analysis)
• Generation of images and texts (e.g. dall · e, stable diffusion, LLM models)
• Autonomous vehicles (e.g. interpretation of sensors, intelligent navigation)
• Personalized recommendations (e.g. Netflix, Amazon, YouTube)

Technological issues

1. Massive data needs 📊
   • Deep Learning requires Huge volumes of annotated data to be efficient.
   • Learning with little data (Few-Shot Learning) It remains a challenge.
2. Calculation power consumption ⚡
   • Model training is based on GPU/Tensor Processing Units (TPU)consuming a lot of energy.
   • Optimization ofinference is essential for large -scale deployment.
3. Explanability of models 🔍
   • Deep neural networks are often considered as “Black boxes”.
   • The methods ofExplainable AI (xai) try to improve their interpretability.
4. IA bias and ethics ⚖️
   • The models can integrate biases present in the training data.
   • Audit and regulation of AI models are major challenges.

Deep Learning vs Machine Learning: What difference?

The future of deep learning

• Development of more economical models in calculation and data.
• On -board AI To integrate the Deep Learning into mobile and IoT devices.
• Hybrid models combining different approaches (symbolic + deep learning).
• Improved explanability For more transparent adoption in critical fields.