Deep learning is a subcategory of machine learning, but what sets it apart is the use of deep neural networks , which include many layers between the input and output. With these multiple layers, deep learning models can learn hierarchical and highly complex representations of data.
Why “deep”?
The term “deep” refers to the number of hidden layers in the neural network. Unlike traditional neural networks (with one or two layers), deep learning involves dozens or even hundreds of layers that allow the model to better capture the complexity of the data.
The main algorithms used in deep learning
- Convolutional Networks (CNN) : As mentioned earlier, these networks are particularly effective for image processing. They are used for tasks like facial recognition, image classification or medical image analysis.
- Recurrent Neural Networks (RNN) : These networks are used for sequential data, such as text or time series. A derivative of RNN, called LSTM (Long Short-Term Memory) , is commonly used to better handle long-term dependencies in sequences.
- Autoencoders : These networks are used to compress data and learn a reduced representation of the input. They are often used for dimensionality reduction or anomaly detection.
- Generative Adversarial Networks (GANs) : Very popular for creating realistic images and videos, these networks work by having two networks compete: a generator and a discriminator, to create synthetic data that resembles real data.
Training deep learning networks
Deep learning typically requires large amounts of data to perform well. It uses advanced techniques like gradient descent and backpropagation to adjust the weights of neurons and minimize the error. Additionally, training these models requires a lot of computing power, which is why GPUs ( graphics processing units) or TPUs (tensor processing units) are often used to speed up the process.
Applications du deep learning
- Self-driving cars : Self-driving systems use convolutional networks to analyze images in real time, identify objects, and make decisions based on this data.
- Speech recognition and machine translation : Deep networks power technologies like Google Translate and speech recognition systems to understand and generate language.
- Medical : Deep learning is used to detect anomalies in medical images, such as cancers or heart disease, with an increasingly high accuracy rate.
- Creative Industry : GANs are being used to create AI-generated artwork, videos or music, paving the way for unique digital creations.
The challenges of deep learning
The main challenge of deep learning is the need for large amounts of data and computational resources. Additionally, these models can be black boxes , meaning it is sometimes difficult to understand exactly why a network makes a certain decision.
Deep learning is a booming field today, with virtually limitless applications in almost every industry.