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Neural Networks Explained Simply — Part 1

  • Neural Networks are machine learning models inspired by the human brain, made of layers of neurons that process data.
  • Each neuron in a Neural Network takes inputs, weights them, adjusts flexibility with bias, and uses activation functions to determine the output.
  • Weights in a Neural Network decide how much importance each input holds, while bias serves as a small adjustment, akin to a backup.
  • Forward Propagation involves data moving through the network to produce output, while Backward Propagation allows the model to learn, adjust weights and biases, and reduce errors over time.

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