Deep Learning Fundamentals

Deep learning is a subfield of ML that focuses on training artificial neural networks to learn and make predictions or decisions. It is called deep learning because it involves training models with multiple layers, allowing them to learn hierarchical representations of data.

The fundamental building block of deep learning is the artificial neural network (ANN). ANNs are inspired by the structure and functioning of biological neural networks in the human brain. An ANN consists of interconnected nodes, called artificial neurons or units organized into layers. The layers are typically divided into an input layer, one or more hidden layers, and an output layer.

Deep learning has achieved remarkable success in various domains, including computer vision, natural language processing, speech recognition, and many others. By leveraging the power of neural networks with multiple layers, deep learning models can automatically learn complex features and representations from data, enabling them to solve intricate tasks and make accurate predictions.

Artificial neural network structure

The following diagram provides a simplified example of how ANNs are structured: