Machine learning technique using multilayered artificial neuron networks to analyze complex data such as images, sound or text.
🎯 Why it matters
It is this precise technology that has enabled the dramatic breakthroughs of recent years, from facial recognition to the generation of realistic texts. Understanding deep learning makes it possible to distinguish simple statistical AIs from advanced systems capable of processing unstructured data. This is the keystone of the General AI revolution.
💡 Concrete example
Midjourney uses deep learning to generate images from text descriptions.
⚙️ In practice
Deep learning is based on architectures of artificial neuron networks with many hidden layers. Each layer extracts increasingly abstract characteristics from the input data: for an image, the first layers detect lines, the following forms, and the last identify a complete object. The training of these models requires colossal computing power, usually provided by graphics cards (GPUs), and optimization algorithms to adjust billions of parameters.
Common mistake: Consolidate machine learning and deep learning. Deep learning is a subcategory of the first, characterized by the use of complex neuron networks that eliminate the need to manually extract data characteristics.
❓ Frequently asked questions
Because of the huge number of internal connections, it is extremely difficult for humans to understand precisely how and why the model has reached a specific decision.
No, deep learning is greedy in data and calculations. For simple tabular data or limited resources, conventional algorithms often remain faster and easier to interpret.