Artificial Intelligence, Deep Learning, and Neural Networks, Explained

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. In addition, each neuron applies a function or transformation to the weighted inputs, which means that the combined weighted input signal is transformed mathematically prior to evaluating if the activation threshold has been exceeded. Architecturally, an artificial neural network is modeled using layers of artificial neurons, or computational units able to receive input and apply an activation function along with a threshold to determine if messages are passed along. Models can become increasingly complex, and with increased abstraction and problem solving capabilities by increasing the number of hidden layers, the number of neurons in any given layer, and/or the number of paths between neurons.