ANN (Artificial Neural Network) Structure: Fully connected layers where each neuron connects to every neuron in the next layer. Best for: Tabular data, classification, regression, and general-purpose tasks. How it works: Takes flattened input → processes through hidden layers → produces output. Weakness: Doesn't preserve spatial or sequential relationships; treats all inputs equally. Example: Predicting … Continue reading Difference between ANN, CNN and RNN