Lesson 4: Introduction to Neural Networks

Learning Objectives

• Understand the biological inspiration for artificial neural networks and how they mimic neuron structure and function
• Explain the architecture of neural networks including input layers, hidden layers, output layers, weights, and biases
• Implement a simple neural network using Python to solve a classification problem
• Analyze how training works through forward propagation, loss calculation, and backpropagation

Standards Alignment

• CSTA 3B-AP-08: Describe how artificial intelligence drives many software and physical systems
• CSTA 3B-AP-16: Demonstrate code reuse by creating programming solutions using libraries and APIs
• Common Core Math HSF-IF.C.7: Graph functions expressed symbolically and show key features of the graph
• NGSS HS-LS1-2: Develop and use a model to illustrate the hierarchical organization of interacting systems

Materials Needed

• Computer with Python 3.x installed (one per student or pair)
• Jupyter Notebook or Google Colab access
• Required libraries: NumPy, Matplotlib, scikit-learn (installation guide provided)
• Neuron diagram handout (biological vs. artificial comparison)
• Network architecture visualization poster
• Dataset: Iris flowers or MNIST digits (included in starter code)
• Projector for live coding demonstrations

Lesson Procedure

1. Hook - The Pattern Recognition Challenge (10 minutes)

   Present students with pattern recognition tasks that humans do effortlessly but were historically difficult for computers:

   • Recognize handwritten digits with varied styles
   • Identify objects in cluttered images
   • Distinguish between cat and dog photos

   Discussion: "How does your brain know this is a '7' even though everyone writes it differently? Today we'll learn how to build artificial systems that can learn patterns like your brain does."

2. Biological Inspiration - From Brain to Code (20 minutes)

   Part 1: Biological Neurons

   • Structure: dendrites (receive signals), cell body (processes), axon (transmits)
   • Function: electrical signals, threshold activation, network connections
   • Learning: synaptic plasticity, strengthening/weakening connections

   Part 2: Artificial Neurons (Perceptrons)

   • Inputs (like dendrites) with weights (connection strength)
   • Weighted sum + bias (like cell body processing)
   • Activation function (threshold, like firing/not firing)
   • Output (like axon transmission)

   Mathematical Model: output = activation(Σ(input × weight) + bias)

   Use the comparison handout to show side-by-side diagrams. Emphasize: Neural networks are inspired by biology but work quite differently!

3. Network Architecture Exploration (15 minutes)

   Layer Types:

   • Input Layer: Receives raw data (pixels, features, etc.)
   • Hidden Layers: Extract increasingly complex patterns
   • Output Layer: Produces final prediction or classification

   Key Concepts:

   • Weights: Strength of connections between neurons, learned during training
   • Biases: Threshold adjustments for each neuron
   • Activation Functions: ReLU, Sigmoid, Tanh - introduce non-linearity
   • Deep Learning: Networks with multiple hidden layers

   Draw network diagrams on board showing information flow. Use interactive visualization tools if available (TensorFlow Playground).

4. Hands-On Coding - Build Your First Network (45 minutes)

   Guided Coding Exercise: Create a neural network to classify iris flowers

   Step 1: Setup and Data Loading

   • Import libraries: NumPy, scikit-learn
   • Load Iris dataset
   • Explore data: features, labels, dimensions
   • Split into training and testing sets

   Step 2: Build the Network

   • Define network architecture: input size, hidden layers, output size
   • Initialize weights and biases randomly
   • Implement activation function (ReLU, Sigmoid)
   • Code forward propagation

   Step 3: Training Process

   • Define loss function (cross-entropy)
   • Implement gradient descent
   • Code backpropagation (or use built-in library)
   • Train for multiple epochs, track loss

   Step 4: Evaluation and Visualization

   • Test on held-out data
   • Calculate accuracy
   • Plot training loss over time
   • Visualize decision boundaries

   Teaching Strategy: Code together, explaining each section. Pause for questions. Students type along. Checkpoint: ensure everyone's code runs before moving forward.

5. Experimentation and Analysis (20 minutes)

   Students modify their neural networks to explore how architecture affects performance:

   Experiments to Try:

   • Change number of hidden layers (1 vs. 2 vs. 3)
   • Adjust neurons per layer (10 vs. 50 vs. 100)
   • Try different activation functions
   • Modify learning rate
   • Increase/decrease training epochs

   Analysis Questions:

   • How does network depth affect accuracy?
   • What happens with too few or too many neurons?
   • Can you identify overfitting or underfitting?
   • What is the training time tradeoff?

   Students record observations in lab notebook format. Share interesting findings with class.

6. Reflection and Real-World Applications (10 minutes)

   Discussion: "Now that you understand how neural networks work, where do you see them in the real world?"

   Applications to Explore:

   • Image recognition (medical diagnosis, facial recognition, autonomous vehicles)
   • Natural language processing (translation, chatbots, voice assistants)
   • Recommendation systems (Netflix, Spotify, Amazon)
   • Game playing (chess, Go, video games)
   • Scientific research (protein folding, climate modeling)

   Exit Ticket: "Explain in your own words how a neural network learns. What surprised you most about how they work?"

Assessment Strategies