Neural Network Essentials: Backpropagation and CNNs Explained

Posted on Aug 7, 2025 in Computer Engineering

Artificial vs. Biological Neural Networks

This section compares Artificial Neural Networks (ANNs) with Biological Neural Networks (BNNs).

  • ANNs are artificial models that mimic some aspects of the brain’s functionality but often lack its flexibility and efficiency.
  • BNNs are naturally evolved networks capable of learning, adapting, and making complex decisions far beyond current AI capabilities.

The Backpropagation Algorithm is a fundamental method used to train artificial neural networks. It adjusts the network’s weights and biases to minimize the error in predictions by propagating errors backward from the output layer to the input layer.

The Backpropagation Algorithm

Steps of the Backpropagation Algorithm:

  1. Forward Pass:

    • Input data is passed through the network.
    • Each neuron processes the data and sends output to the next layer.
    • The final layer produces the network’s prediction.

  2. Compute the Loss:

    • The difference between the predicted output and the actual output (error) is calculated using a loss function (e.g., Mean Squared Error for regression, Cross-Entropy for classification).

  3. Backward Pass (Gradient Calculation):

    • The error is propagated backward through the network using the chain rule of differentiation.
    • The gradients of the loss function with respect to each weight are calculated.

  4. Weight Update (Gradient Descent):

    • The weights are adjusted using gradient descent (or its variants like Adam, RMSprop).
    • Formula: w = w - η * (∂L/∂w) where:
      • w = weight,
      • η = learning rate,
      • ∂L/∂w = gradient of the loss with respect to the weight.

  5. Repeat:

    • The process repeats for multiple epochs (iterations over the dataset) until the error is minimized.

Why is Backpropagation Important?

  • Enables deep learning models to learn complex patterns.
  • Efficiently optimizes large neural networks.
  • Works well with different architectures (CNNs, RNNs, Transformers).

Understanding Convolutional Neural Networks (CNNs)

A Convolutional Neural Network (CNN) is a type of deep learning model primarily used for analyzing visual data, such as images and videos. CNNs are widely applied in computer vision tasks like image classification, object detection, and facial recognition.

Key Components of a CNN

  1. Convolutional Layers – These layers apply filters (kernels) to extract features like edges, textures, and patterns from images.
  2. Pooling Layers – These layers reduce the spatial dimensions of the feature maps, making computations more efficient and helping prevent overfitting. Common pooling techniques include max pooling and average pooling.
  3. Activation Functions – Typically, ReLU (Rectified Linear Unit) is used to introduce non-linearity, allowing the network to learn complex patterns.
  4. Fully Connected Layers – These layers connect every neuron to the next layer and help in making the final classification or prediction.
  5. Dropout – A regularization technique that randomly deactivates some neurons during training to prevent overfitting.

How CNNs Work

  1. Input Layer: The image is fed into the network as pixel values.
  2. Feature Extraction: Convolutional and pooling layers extract key features.
  3. Flattening: The output from the feature extraction layers is converted into a 1D vector.
  4. Classification: The fully connected layers and the softmax function determine the final class label.

Applications of CNNs

  • Image Classification (e.g., recognizing handwritten digits in MNIST dataset)
  • Object Detection (e.g., YOLO, Faster R-CNN)
  • Facial Recognition (e.g., Face ID)
  • Medical Image Analysis (e.g., tumor detection in MRI scans)
  • Autonomous Vehicles (e.g., detecting pedestrians and obstacles)