Ai Neural Networks — Free AI & ML Tutorial
Learn Ai Neural Networks in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Neural Networks in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Neural Networks in AI & ML
A Neural Network is a computational system inspired by the human brain. Just as your brain has billions of neurons connected by synapses, a neural network has nodes (artificial neurons) connected by weights.
Structure: Input Layer (receives data) → Hidden Layers (process and transform) → Output Layer (gives result). Each connection has a "weight" — a number that determines how much influence that connection has.
Learning happens by adjusting weights. If the network makes a wrong prediction, a process called Backpropagation adjusts weights to make it slightly less wrong. Repeat millions of times, and the network becomes accurate.
A single neuron does this: takes several inputs, multiplies each by its weight, adds them up, adds a bias, then passes through an "activation function" that decides whether and how strongly to "fire".
Ai Neural Networks — Syntax
# Single neuron (perceptron) formula: # output = activation(w1*x1 + w2*x2 + ... + wn*xn + bias) # # Common activation functions: # ReLU: max(0, x) — most used in hidden layers # Sigmoid: 1/(1+e^-x) — outputs 0 to 1 (for binary classification) # Softmax: for multiple categories
Learn Ai Neural Networks step by step with Syllab's free interactive AI & ML tutorial — runnable code examples, practice exercises and instant AI feedback, all free with no signup. Explore the full AI & ML course →