Artificial Neural Networks (ANNs)

• Artificial Neural Networks (ANNs) are a subset of machine learning models designed to simulate the way the human brain processes information. 
• These networks are capable of recognizing patterns, making decisions, and solving complex problems by mimicking biological neural networks.

Working Principle:

• The structure of an ANN is inspired by the human brain, consisting of artificial neurons (nodes) arranged in layers.
• These neurons process input data, apply weights, and transmit signals to the next layer using activation functions.
• ANNs learn from data through training, adjusting connection strengths (weights) to improve accuracy.

Types of Artificial Neural Networks

Deep Neural Networks (DNNs):

• Contain multiple hidden layers that allow deep learning models to recognize complex patterns.
• Used in speech recognition, financial modelling, and autonomous systems.

Convolutional Neural Networks (CNNs):

• Primarily used for image and video recognition tasks.
• Detect spatial hierarchies in data, such as edges, textures, and objects in an image.
• Applications: 
• Facial recognition, medical image analysis, self-driving cars.

Recurrent Neural Networks (RNNs):

• Designed for sequential data processing, where past inputs influence future outputs.
• Used in natural language processing (NLP) and time-series forecasting.

Generative Adversarial Networks (GANs):

• Composed of two competing networks: a generator (creates data) and a discriminator (evaluates authenticity).
• Used to generate realistic images, music, and deepfake videos.

Spiking Neural Networks (SNNs):

• More biologically realistic than traditional ANNs, as they simulate neuron firing patterns.
• Used in robotics, neuromorphic computing, and energy-efficient AI systems.

Comparison: Brain’s Neural Network vs. Artificial Neural Network