Neural Networks: The Building Blocks of Deep Learning

Artificial Neural Networks (ANNs) are at the core of deep learning. They are inspired by the structure and function of the human brain, consisting of interconnected nodes (neurons) that process information.

Key components of a neural network include:

• Neurons: Fundamental units that receive input, apply a mathematical transformation, and pass the output to the next layer.
• Weights and Biases: Adjustable parameters that determine the strength of connections between neurons.
• Activation Functions: Functions like ReLU, Sigmoid, and Softmax that introduce non-linearity, enabling the network to learn complex patterns.

Neural networks can have multiple layers, leading to deep learning architectures that outperform traditional machine learning methods in tasks like speech recognition, autonomous navigation, and medical diagnostics.