Neural networks aren’t magic — they’re trillion‑parameter math engines pretending to think. 🧠⚡✨ Each neuron takes numbers in, multiplies by learned weights, adds a bias ➕, passes through a nonlinearity 🔄, and pushes activations forward ⚡. Stacked across thousands of layers and trillions of parameters, this becomes a gigantic function approximator that models language, images, code, and reasoning as high‑dimensional patterns 🗣️🖼️📜. During training, gradient descent 📉 and backpropagation 🔄

Insightforge | AI & Data Science on Instagram: "Imagine you’re working with a dataset containing two classes, but the data isn’t linearly separable. In such cases, simple linear models like logistic regression or linear SVMs struggle to create an accurate decision boundary. This is where neural networks excel. By introducing nonlinearity through activation functions such as ReLU, sigmoid, or tanh, they transform the input space layer by layer, allowing the model to learn much more complex patter

What Is Regularization? | IBM

Regularization: Simple Definition, L1 & L2 Penalties

L1 and L2 Regularization Methods, Explained | Built In

L2 vs L1 Regularization in Machine Learning | Ridge and Lasso Regularization

The Best Guide to Regularization in Machine Learning | Simplilearn