What is a Neural Network?

A Neural Network (NN) is a computational model inspired by the human brain’s structure and function. It consists of interconnected nodes (neurons) organized in layers:

  • Input Layer: Receives raw data (e.g., pixels in an image).
  • Hidden Layers: Process data through weighted connections and activation functions.
  • Output Layer: Produces the final prediction (e.g., classifying an image as "cat" or "dog").

Neural networks excel at recognizing patterns in complex data, making them essential for tasks like image recognition, natural language processing, and autonomous driving.

How Neural Networks Learn

Learning in neural networks involves adjusting weights to minimize prediction errors. Here’s the step-by-step process:

1. Initialization: Starting with Random Weights

  • All connections between neurons are assigned random weights (small values near zero).
  • Example: In facial recognition, initial weights mean the network guesses randomly—like a newborn seeing faces for the first time.

2. Forward Propagation: Making Predictions

  • Data flows from input → hidden layers → output.
  • Each neuron calculates a weighted sum of inputs, then applies an activation function (e.g., ReLU, Sigmoid) to introduce non-linearity.
  • Output: The network’s prediction (e.g., "This image is 70% likely to be Person A").

3. Calculating Error: The Cost Function

  • The cost function (e.g., Mean Squared Error, Cross-Entropy Loss) measures how far predictions are from actual values.
  • Example: If the network mislabels Person A as Person B, the cost (error) is high.

4. Backpropagation: Learning from Mistakes

  • The network propagates the error backward through layers.
  • Using gradient descent, it calculates how much each weight contributed to the error and adjusts weights to reduce future errors.
  • Key Insight: Larger errors trigger bigger weight adjustments.

5. Iterative Training: Repeat Until Convergence

  • Steps 2–4 repeat across thousands of examples (training epochs).
  • Over time, weights stabilize, and predictions improve.

Key Concepts in Neural Network Learning

  • Learning Rate: Controls the size of weight adjustments (too high → overshooting; too low → slow learning).
  • Overfitting: When a model memorizes training data but fails on new data (solved via regularization/dropout).
  • Batch vs. Stochastic Training: Updates weights after a batch of examples (batch) or per example (stochastic).

Real-World Example: Facial Recognition

  1. Training: The NN analyzes thousands of face images, adjusting weights to distinguish features (e.g., eye spacing).
  2. Testing: It identifies faces in new videos by comparing patterns to learned weights.
  3. Improvement: Misclassifications trigger further weight tuning via backpropagation.

Conclusion

Neural networks learn by iteratively refining weights to minimize errors, mimicking how humans learn from experience. From voice assistants to medical diagnoses, their ability to self-improve through data makes them transformative in AI.

Next Steps: Explore activation functions, convolutional NNs for images, or transformers for language tasks!