Neural Networks: The Building Blocks Explained 1. What a single neuron is A neuron is just a linear equation. Output = weight × input + bias 2. What the neuron’s goal is Find the right values for weight and bias So the line fits the data well. This search for correct values is called learning. 3. How learning actually starts The neuron does not know the correct values. So it starts with random weight and bias values. In PyTorch, these are called model parameters. 4. How a neuron knows it’s wrong The model makes a prediction. We compare it with the actual value. The difference between them is the error. This error is measured mathematically. 5. How the neuron improves Using the error, the model slightly adjusts the weight and bias values. Smaller error → better parameters. 6. What happens when neurons increase More neurons do not mean complex math. Each neuron still performs only a linear calculation. Example: If one neuron computes (w₁x + b₁), multiple neurons compute many such equations in parallel. 7. What a neural network really is A neural network is neurons connected to neurons. It has the following, Input layer: raw data Hidden layers: internal computations Output layer: final prediction Hidden layers are learned automatically. We don’t manually set their values. Key takeaway: Neural networks look complex, but they are built from simple linear neurons learning better parameters step by step.