Neural networks are a subset of artificial intelligence, inspired by the structure and function of the human brain. They consist of layers of interconnected nodes, or neurone that process data and learn to recognise patterns through training.
- Neurons: The basic units of a neural network, similar to biological neurons.
- Weights: Numerical values assigned to connections between neurons, determining the influence of one neuron on another.
- Activation functions: Mathematical functions applied to the output of a neuron to introduce non-linearity.
- Bias: A constant value added to the input of a neuron to adjust the output.
- Input layer: Receives data.
- Hidden layers: Process information through complex calculations.
- Output layer: Produces results.
Information flows through these layers, and the network learns by adjusting the connections (weights) between neurons to improve accuracy over time.
- Structure: Composed of an input layer, one or more hidden layers, and an output layer.
- Training: Involves feeding the network large amounts of data and adjusting the weights of the connections between neurons to minimize error.
- Activation Functions: Non-linear functions applied to the input of each neuron, enabling the network to learn complex patterns.
- Back propagation: A method used to update the weights by calculating the gradient of the error with respect to each weight.
- Feedforward Neural Networks (FNN): The simplest type, where connections do not form cycles, used for straightforward tasks like image recognition.
- Convolutional Neural Networks (CNN): Specialize in processing grid-like data such as images, excelling in image and video recognition tasks.
- Recurrent Neural Networks (RNN): Designed for sequential data, such as time series or text, capable of handling variable input lengths.
- Long Short-Term Memory Networks (LSTM): A type of RNN that can learn long-term dependencies, useful in tasks like language modeling and speech recognition.
- Generative Adversarial Networks (GANs): Consist of a generator and a discriminator network, used for generating realistic data such as images.
- Healthcare: Disease diagnosis, personalized treatment plans, predictive analytics, and drug discovery.
- Natural Language Processing (NLP): Language translation, sentiment analysis, and chatbots.
- Automotive: Autonomous driving, traffic management, and predictive maintenance.
- Finance: Stock market prediction, fraud detection, risk assessment, and algorithmic trading.
In essence, neural networks are powerful tools that enable computers to learn from data and make decisions, mimicking human cognitive abilities.
