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Neural Networks – Intuitively and Exhaustively Explained
- Neural networks take inspiration from the communication between neurons in the human brain, which occurs from an electrochemical signal between neurons.
- Neural networks consist of mathematically simplified neurons called perceptrons, which aggregate data and output a signal based on input.
- Data scientists use activation functions, such as ReLU, Sigmoid and Softmax to inject non-linearity in neural networks.
- Back propagation is an algorithm used to train neural networks by comparing predictions to desired outputs, then updating the model based on the difference between the output and desired answer.
- Normalization is a method used to scale inputs and outputs in a neural network to a range averaging around zero to avoid values that are too small or large.
- This detailed article explores the concepts of neural networks from theory to implementation using NumPy, a numerical computing library.
- Training a neural network involves passing training data through the model and comparing predicted outputs to known outputs, updating the model based on the difference.
- Increasing the amount of training data or adjusting the regularization parameters can help enhance predictions, but advanced approaches are required to achieve more consistent results.
- The article aims to be accessible to beginners, offering a thorough understanding of neural networks while delving into more advanced concepts.
- Future articles will continue to explore more advanced applications of neural networks, zeroing in on subjects like annealing, dropout, and gradients.
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