The world of artificial intelligence is vast—and above all, very technical. You can use AI without knowing how it works, but if you’re used to our articles on Yiaho and our glossary, it’s because you want to learn more about artificial intelligence! Let’s discover together what Deep Learning is.
What is Deep Learning?
Deep Learning, or “deep learning” in French, is a subcategory of artificial intelligence and machine learning (“apprentissage automatique”).
It’s a method that uses artificial neural networks to learn from large amounts of data. This type of learning is inspired by how the human brain works, where neurons interact with each other to process information.
Neural networks are made up of several layers of neurons (or processing units) that extract increasingly complex features from data. The term “deep” in Deep Learning refers to the depth of these networks—that is, the number of layers they contain.
How does Deep Learning work?
- Data collection: To train a Deep Learning model, you first need to gather a large dataset. For example, to recognize images of motorboats and sailboats, we would need thousands of images of each type of boat.
- Data preprocessing: Data often needs to be prepared and normalized. This can include resizing images, converting colors, or cleaning text for natural language processing.
- Model training: Next, the Deep Learning model is trained. During this phase, the data is fed into the neural network. Each neuron performs simple calculations, and the results are passed to the next layers. The model adjusts its internal parameters based on the errors it makes, thanks to a process called backpropagation.
- Evaluation and optimization: After training, the model’s performance is evaluated on a separate dataset (called a test set). If the model doesn’t perform well, adjustments can be made to improve its accuracy.
Also read: OpenAI introduces o3: its new and best AI
Deep Learning vs. Machine Learning
Deep Learning and Machine Learning are often confused, but they have significant differences in how they work and in their applications.
Machine Learning is generally effective with smaller datasets and often requires manual feature extraction, while Deep Learning excels at handling large amounts of unstructured data, such as images, videos, or text.
In short, Deep Learning can be seen as an advancement of Machine Learning, making it possible to tackle more complex problems thanks to a more sophisticated model structure and an ability to learn autonomously.
Simple examples of Deep Learning
- Image recognition: Imagine you have a smartphone that can identify people in your photos. Thanks to Deep Learning, the phone can analyze each image using a neural network to distinguish facial features and recognize who is in the photo.
- Machine translation: Translation apps, like Google Translate, also use Deep Learning. By analyzing millions of sentences in different languages, the model learns to translate expressions while respecting context and meaning.
- Voice assistant: Voice assistants like Siri or Alexa work thanks to Deep Learning. They turn your voice into text, analyze what you say, and generate an appropriate response using natural language understanding.
An important technology for AI
Deep Learning is a powerful technology that is transforming many fields, from computer vision to language understanding.
By using deep neural networks, it enables machines to learn and improve from massive amounts of data, offering innovative and intelligent solutions to complex problems.
