What is deep learning?
Deep learning is a machine learning approach based on neural networks with many layers. These networks learn representations from data: edges and shapes in images, sounds in speech, word relationships in text, or hidden patterns in business data.
Important architectures
| Architecture | Used for | Typical value |
|---|---|---|
| Feed-forward networks | Structured data and baseline neural models. | Flexible function approximation. |
| CNNs | Images, remote sensing, visual inspection. | Learn spatial features. |
| RNNs / LSTMs | Sequences and time-series. | Handle order and temporal patterns. |
| Transformers | Language, vision, code, multimodal tasks. | Handle long-range relationships with attention. |
Why deep learning became powerful
Deep learning works well when there is enough data, compute, architecture quality, and training discipline. It powers many modern AI systems, including language models, speech recognition, image classification, object detection, code generation, and multimodal systems.
Constraints to respect
- It can require significant data and compute.
- It may be difficult to explain.
- It can overfit or fail when data changes.
- It must be evaluated against real-world errors and risks.
- It needs careful deployment and monitoring.
How to learn deep learning
Start with Python, linear algebra, probability, gradient descent, neural network basics, then PyTorch or TensorFlow. Build small projects: image classifier, text classifier, embedding search, sequence forecast, then an LLM/RAG prototype.