Introduction to the Mathematics Behind Neural Networks ? article

this will be part 1 and we will investigate it.

* Neural networks, inspired by the human brain, consist of layers of neurons working together to make predictions. The math behind these networks involves:

1. Linear Algebra: Neurons perform calculations using matrix multiplication, involving weights and biases.

2. Forward Propagation: Inputs move through the network, with neurons applying a weighted sum and activation functions like ReLU or Sigmoid to produce outputs.

3. Loss Function: Measures how far the network's predictions are from the actual values, using functions like Mean Squared Error (MSE). ️

4. Backpropagation & Gradient Descent: Adjusts the weights to minimize the loss, optimizing the network’s accuracy.

Grasping these mathematical concepts is key to building and fine-tuning powerful neural networks that can learn and improve over time. ?