Artificial Neural Networks, a core concept in AI, model the human brain’s interlinked neurons to learn patterns and tackle problems. Through multiple layers of interconnected nodes, they process, learn from, and make decisions based on data.
Imagine you’re playing a guessing game, like “20 Questions”, but it’s your brain against a computer. The computer starts guessing wildly at first, learns from wrong guesses, and gradually gets better. That’s kind of what’s happening with Artificial Neural Networks - a lot of computer “neurons” starting out with wild guesses and getting better by learning from their mistakes.
Artificial Neural Networks (ANNs) are computing systems inspired by the biological neural networks found in human brains. An ANN comprises interconnected artificial neurons, or nodes, which mimic biological neurons. The connections between these artificial neurons are called “edges”, each possessing a “weight” that adjusts as learning proceeds, influencing the data that is being transmitted.
The structure of an ANN incorporates multiple layers - namely, the input layer, one or more hidden layers, and the output layer. The input layer takes in raw data for the network to learn from, akin to the sensory input for humans. The hidden layers are where the deep learning takes place. They extract features, perform computations, and process values sent from the input layer. Eventually, the output layer translates the results from the hidden layers into a format that makes sense for the problem scenario.
The process in an ANN involves data flowing from the input layer through the hidden layers to the output layer in a “feedforward” manner. It also involves a backpropagation process, a learning algorithm that adjusts the weights of the neurons based on the error in the output. This is how an ANN learns and adapts to improve future predictions or classifications.
An important feature of ANNs is the activation functions which decide whether a neuron should be activated or not. This “decision” is based on the weighted sum of the input and the bias. Some common activation functions include the Sigmoid, Tanh, and ReLU (Rectified Linear Unit).
ANNs are versatile and are used in conducting complex mathematical and logical operations, recognizing patterns, and making predictions. They are particularly useful in domains like image recognition, speech recognition, natural language processing, and making complex predictions in fields like stock market analysis or disease diagnosis.
Keep in mind though, training ANNs require large amounts of data and computation power. And while ANNs can learn and mimic nonlinear relationships, interpreting these models can sometimes be a challenge due to their opaque ‘black-box’ nature.
Feedforward Neural Networks, Backpropagation, Activation Function, Deep Learning, Bias, Neuron, Node, Hidden Layer (Hidden Layer),, Input Layer (Hidden Layer),, Output Layer (Hidden Layer),, Weights, Supervised Learning, Unsupervised Learning