Neural networks are at the heart of deep learning.

Inspired (loosely) by how our brains work, these algorithms excel at recognizing patterns, whether it’s identifying objects in images or translating speech into text.

In this section, we’ll discuss the anatomy of a neuron, how forward and backward passes train neural networks, and why hidden layers make deep learning so powerful.

Basic Anatomy of a “Neuron”

• Inputs and Weights

  • Each neuron takes in input values (like pixels of an image) multiplied by weights.

  • Weights determine how important each input is—large weights amplify certain inputs, while small weights dampen them.

• Activation Function

  • After summing up the weighted inputs, the neuron applies an activation function (e.g., ReLU, sigmoid) to introduce nonlinearity.

  • This nonlinearity is crucial because it allows the network to learn complex patterns rather than just fitting a straight line.

• Output

  • The result of the activation function becomes the neuron’s output, which can be passed on to other neurons or form the final prediction.

Think of a neuron as a gate.

You supply various “signals” (inputs), each with its own importance level (weights). The gate sums these signals, applies a simple rule (activation), and outputs a final signal—either strong or weak.

Forward Pass vs. Backward Pass

1. Forward Pass

   • The data moves forward through the network: from input layer → hidden layers → output layer.

   • Each neuron computes its output, which becomes the input for the next layer.

   • At the final layer, you get a prediction—like a probability of whether an image contains a cat or a dog.

2. Backward Pass (Backpropagation)

   • After the forward pass, the model compares its prediction with the true label (e.g., “cat” or “dog”).

   • This difference (the loss) is measured by a loss function (like MSE for regression or cross-entropy for classification).

   • Gradient Descent: The algorithm calculates how much each weight contributed to the error, then adjusts those weights in the opposite direction of the gradient (the steepness) to minimize the loss.

   • These weight updates flow backward through the network, hence the name “backpropagation.”

This loop of forward prediction and backward correction is what enables learning.

Each iteration (epoch) refines the weights, ideally leading to better performance with each pass.

The Significance of Hidden Layers and “Deep” Networks

• Hidden Layers

  • Located between the input and output layers, these layers transform raw inputs into more abstract, useful representations.

  • For example, in image recognition, early hidden layers might detect edges, corners, or simple textures, while deeper layers detect more complex features like faces or objects.

• Depth (Multiple Hidden Layers)

  • A deep neural network has many hidden layers stacked on top of each other.

  • More layers allow the network to build up hierarchical representations, capturing increasingly sophisticated patterns.

  • However, training very deep networks can be computationally expensive and requires techniques like careful initialization, specialized activation functions, and sometimes large datasets.

Main Idea: The “depth” in deep learning isn’t just about having a bigger model; it’s about enabling the system to learn complex, layered abstractions in the data.

That’s why deep networks have revolutionized fields like computer vision, natural language processing, and speech recognition.