Artificial Neural Network

An artificial neural network (ANN) is a machine learning model loosely inspired by the biological neurons in the human brain. It consists of layers of interconnected nodes (neurons) that transform inputs into outputs by passing signals forward through weighted connections.

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How it works

• Input layer: receives raw data (pixels, words, numbers).
• Hidden layers: perform computations, extracting increasingly abstract patterns.
• Output layer: produces the final prediction, such as a classification label.
  During training, the network uses algorithms like backpropagation and gradient descent to adjust weights and reduce prediction error.

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Applications

• Image recognition: classifying objects, faces, handwriting.
• Speech and NLP: language translation, chatbots, voice assistants.
• Healthcare: diagnostic imaging and early disease detection.
• Autonomous systems: navigation for self-driving vehicles.

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Why it matters

ANNs are the foundation of modern deep learning. With enough layers and data, they can model highly complex, non-linear relationships, making them crucial for today’s breakthroughs in AI.

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Artificial Neural Networks (ANNs) represent the foundation of modern deep learning. Their strength lies in their ability to approximate highly complex and non-linear relationships, making them suitable for problems where explicit programming is impossible. Thanks to the universal approximation theorem, we know that even relatively simple neural networks can, in theory, approximate any function given enough neurons and data.

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Over time, ANNs have evolved from early shallow architectures to deep networks with dozens or even hundreds of layers. Innovations such as convolutional neural networks (CNNs) for vision, recurrent neural networks (RNNs) and transformers for sequential data, and graph neural networks (GNNs) for structured information highlight how the neural network paradigm has adapted to different data modalities.

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At the same time, neural networks are often described as black boxes, since it is not always easy to explain how they reach their decisions. This has fueled a growing interest in explainable AI (XAI) and interpretability techniques, such as saliency maps or SHAP values, especially in sensitive areas like healthcare or finance.

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📚 Further Reading

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
• Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks.