AI Fundamentals Course (AI101) – Lesson7

🎓 Lesson 7: Neural Networks and How They Work

---

Lesson Objective:

To help learners understand what Artificial Neural Networks (ANNs) are, how they are inspired by the human brain, and how they function to enable modern AI systems.

---

What Are Neural Networks?

Note: While their full name is Artificial Neural Networks (ANNs), they are commonly referred to as “Neural Networks”, or sometimes “Neural Nets”

A Neural Network is a type of machine learning model that is inspired by the structure and function of the human brain.

Just like our brains have neurons connected by synapses, a neural network consists of digital “neurons” arranged in layers — and they learn by passing signals between them.

Neural Networks allow machines to learn from complex data like images, sound, and language.

---

The Inspiration: The Human Brain

Your brain contains about 86 billion neurons.
Each neuron receives signals, processes them, and sends outputs to other neurons.

A neural network simulates this process digitally, with layers of artificial neurons that:

• Receive input (data)

• Process it using weighted connections

• Pass results to the next layer

• Output a prediction or decision

---

Basic Structure of a Neural Network

1. Input Layer – Receives the data (e.g., pixels in an image or words in a sentence)

2. Hidden Layers – Perform calculations and learn patterns

3. Output Layer – Produces the final answer or prediction

Example:
For a photo of a cat, the output layer might say:
→ 95% Cat, 4% Dog, 1% Other

---

Big Question: How Does a Neural Network Learn?

1. Data is passed through the network.

2. Each connection between neurons has a weight (importance level).

3. The system compares its prediction with the correct answer (label).

4. It adjusts the weights to improve accuracy — a process called backpropagation.

5. This cycle is repeated thousands or millions of times until the network becomes very good at the task.

Think of it as a “trial and error” based learning— but at lightning speed!

---

Real-Life Example: Image Recognition

Let’s say we want to build an AI (using Neural Networks) to recognize cats in photos.

• We show it thousands of labeled images (cat or not-cat).

• The neural network starts learning what a “cat” looks like: eyes, ear shape, whiskers, fur texture, etc. All of these biological features have similar patterns in cats, and neural network learns these patterns (at a pixel level on the images).

• Eventually, after seeing thousands of cats, it can look at a new image it’s never seen before and say: “That’s a cat!”

---

Analogy: Layers of Understanding

Imagine a human learning to recognize a dog:

• First, we see the shape (ears, tail)

• Then, we remember past experiences

• Then, we distinguish it from a wolf or a fox

• Finally, we say “That’s a dog!”

Neural networks go through layers of analysis, just like we do — from raw data to meaning.

---

Business Use Cases of Neural Networks

---

Fun Fact

The neural network behind ChatGPT (the most popular AI program currently) is called a Transformer, which is a special kind of deep neural network designed to understand human language. It has billions of artificial neurons!

Recommended Reading to learn more about Transformers:
https://gsbx.org/transformers-for-deep-learning-ai/

---

Reflection Prompt (for Learners)

• Have you ever used an app or tool (like Google Photos or facial recognition) that may have used a neural network behind the scenes?

---

✅ Quick Quiz (not scored)

1. What is a neural network inspired by?

2. What are the three main parts of a neural network?

3. What is backpropagation?

4. Give one real-world use case for neural networks.

5. True or False: Neural networks are only used for images.

---

Key Takeaway

Neural Networks are the brain behind modern AI.
They allow machines to learn patterns, make predictions, and adapt to complex data — just like how our brains learn from experience.