Deep learning (DL)
Deep learning (DL) is a branch of machine learning that teaches computers to learn from data in a way similar to how humans learn from experience. Instead of relying on manually programmed rules, deep learning models use artificial neural networks made of layers of interconnected nodes, or “neurons,” to detect patterns and relationships in data.
The “deep” in deep learning refers to networks with multiple layers, which allows the model to capture increasingly complex features—earlier layers might detect simple shapes or edges, while deeper layers can recognize entire objects, language patterns, or other intricate structures.
Deep learning models are trained by adjusting the connections between neurons, known as weights and biases, to minimize prediction errors, typically using algorithms like backpropagation and gradient descent.
There are several types of deep learning architectures, each suited to different tasks.
- Convolutional neural networks (CNNs) excel at processing images by detecting local patterns
- Recurrent neural networks (RNNs) are designed for sequential data like speech or time series.
- Transformers have become the backbone of generative AI tools like ChatGPT.
- GANs generate realistic synthetic data by pitting two networks against each other.
The use cases of deep learning include image recognition, natural language processing (NLP), speech and audio processing, computer vision tasks, recommendation systems, medical image analysis and diagnostics, and fraud detection.
