Section outline

  • Neural Networks Simplified

    Neural networks are the heart of many modern AI systems. Although they can be complex mathematically, the core idea is inspired by how our human brain works — learning patterns from data.

    • 🧠 What is a Neural Network?

      A neural network is a series of layers made up of nodes (or "neurons") that process data. Each node takes input, applies a transformation (usually a weighted sum followed by an activation function), and passes it to the next layer. This layered architecture helps neural networks learn to recognize patterns, classify images, detect motion, and more.

      • Input Layer: Takes the raw data (e.g., pixel values from an image).
      • Hidden Layers: Perform intermediate processing; these layers detect complex features by combining earlier patterns.
      • Output Layer: Produces the final result — such as recognizing whether an object is a bottle, can, or plastic.

      🧩 Building Blocks of a Neural Network

      • Weights: Adjustable values that determine the strength of connections between nodes.
      • Biases: Offset values added to the output of a node to increase flexibility.
      • Activation Functions: Decide whether a neuron should fire — common ones include ReLU (Rectified Linear Unit), sigmoid, and softmax.

      🔄 How a Neural Network Learns

      Neural networks are trained through a process called backpropagation, using large datasets and iterative adjustments:

      1. Forward pass: Input is passed through the layers to make a prediction.
      2. Loss calculation: Compares the prediction to the actual label to compute an error.
      3. Backpropagation: The error is propagated backward to adjust the weights using optimization algorithms like gradient descent.

      Over many training cycles (epochs), the network becomes better at minimizing error and making accurate predictions.

      🎯 Why Neural Networks Matter in Robotics

      In robotics, neural networks are used in:

      • Object recognition — identifying and classifying objects using camera input.
      • Gesture recognition — interpreting human hand movements.
      • Voice command processing — understanding audio instructions using speech models.

    • 🔍 Simple Analogy

      Imagine teaching a robot to recognize apples. The neural network sees thousands of apple photos, adjusts its weights, and gradually learns which visual patterns (like shape, color, and texture) define an apple. Once trained, the robot can detect apples in new images it has never seen before.

      🧪 Activity Idea

      Use a pre-trained model from Teachable Machine or TensorFlow Lite to classify different objects. Try retraining it with your own images to see how accuracy changes with more data or better quality photos.