Backpropagation is a technique used in training Artificial Neural Networks. It involves propagating error information backward through the network, allowing adjustments to be made to the network’s parameters. Backpropagation helps improve the accuracy and effectiveness of neural networks in tasks like pattern recognition and prediction.
Bayesian networks are Probabilistic Graphical Models that represent and evaluate uncertainty and conditional dependencies between variables. Industries such as healthcare and finance use Bayesian Networks to handle complex decision-making, risk assessment, and predictive modelling.
Bias in AI refers to systematic errors or prejudices that can occur within AI systems due to biased training data, faulty algorithms, or human biases. Addressing bias in AI is crucial for ensuring fairness, inclusiveness, and ethical practices in AI applications.
Big Data refers to large, complex datasets that cannot be easily managed or analysed with traditional data processing methods. AI techniques, such as Machine Learning, enable businesses to extract valuable insights from Big Data, revealing patterns, trends, and correlations that can inform decision-making and drive growth.
