Deep Dive into Artificial Neural Networks

Abstract

Artificial Neural Networks (ANNs) are computational models inspired by the human brain, used extensively in machine learning for tasks like image recognition and natural language processing. ANNs consist of interconnected nodes (neurons) organized in layers: input, hidden, and output. Each neuron processes input data, applies a weighted transformation, and passes it through an activation function like ReLU or sigmoid.

Training involves backpropagation, where the network adjusts weights to minimize error using gradient descent. Deep learning, a subset of ANNs with multiple hidden layers, powers applications like autonomous vehicles and medical diagnostics.

Challenges include overfitting, computational cost, and interpretability. Techniques like dropout and batch normalization mitigate these issues, while research into explainable AI aims to make ANNs more transparent.

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