تگ های موضوع

ARTIFICIAL NEURAL NETWORKS

Artificial Neural Networks (ANNs) are computational models inspired by the human brain's structure and function. They consist of interconnected nodes, or neurons, that work together to process information. The architecture of ANNs typically includes an input layer, one or more hidden layers, and an output layer.
In the input layer, data enters the network. These inputs, which can be anything from images to sounds, get transformed into numerical values. Each neuron in the hidden layers processes these inputs using weights and biases, which are adjusted during training. The output layer then produces the final result, whether it’s a classification, regression, or any other type of prediction.
LEARNING PROCESS
Training an ANN involves a method called backpropagation. During this process, the network makes predictions and compares them against actual results. If there’s a difference, the network adjusts its weights and biases to minimize this error. This iterative process continues until the network learns to produce accurate outputs.
In addition, various activation functions, like ReLU (Rectified Linear Unit) or sigmoid, introduce non-linearities in the model. This allows ANNs to learn complex patterns and relationships within the data.
APPLICATIONS
Artificial Neural Networks have a vast range of applications. They are widely used in image recognition, natural language processing, and even in game playing. For instance, convolutional neural networks (CNNs), a specific type of ANN, excel in processing visual data. Recurrent neural networks (RNNs), on the other hand, are used for sequential data like time series or language translation.
In summary, ANNs are powerful tools capable of learning from data and making predictions. Their flexibility and adaptability continue to drive advancements in various fields.