Article 3 — Neural Networks explained simply

🧠 Recap

In Article 2, we explored how machines learn from data using Machine Learning. Now let’s uncover the magic behind today’s smartest systems: Neural Networks — the building blocks of modern AI.

🤖 What Are Neural Networks?

A Neural Network is a computer system inspired by the human brain. Just like your brain has neurons that fire and communicate, artificial neural networks use layers of nodes (neurons) to process and learn patterns from data.

Imagine a network of tiny decision-makers, passing signals and adjusting connections to get smarter over time.

🧱 The Basic Structure

A neural network is made of three main layers:

1. Input Layer: Receives the raw data (e.g., pixel values, token embeddings).

2. Hidden Layers: One or more layers that transform data using weights and biases.

3. Output Layer: Produces the final result (e.g., predicted label or next word).
   
   

Each layer is made of neurons connected to the next, and each connection has:

• Weights: Strength of the connection (learned during training)

• Biases: Offsets to adjust activations

⚙️ How It Works

1. Data In: Data (e.g., image, text) is input into the network.

2. Forward Pass: Each neuron receives inputs, applies a weighted sum and activation function, and sends output to the next layer.

3. Prediction Out: The network makes a guess.

4. Training Time: If training, it checks how wrong the guess was and uses backpropagation to update weights.

🧪 Activation Functions

These functions decide whether a neuron should “fire” or not:

• ReLU (Rectified Linear Unit): Most popular; keeps only positive values.

• Sigmoid: Maps values between 0 and 1.

• Tanh: Maps values between -1 and 1.

🔍 Why Neural Networks Work

• They stack layers to learn from simple to complex features.

• In an image classifier, early layers detect edges, deeper layers recognize shapes, and final layers detect objects.

• In language models, early layers understand grammar, deeper ones understand meaning.

🧠 Types of Neural Networks

📌 Real-World Examples

• Image Recognition: CNNs detect cats, tumors, and more.

• Language Translation: RNNs and Transformers understand and generate text.

• Speech Recognition: RNNs convert audio to text.

• Recommendation Systems: MLPs and embeddings power Netflix and Amazon.

📚 Coming Up in Article 4

Next, we’ll explore Transformers — the revolutionary architecture behind ChatGPT, Google Translate, and many AI breakthroughs.

🔑 Key Takeaway

Neural Networks are the core of deep learning. They mimic the brain, process data in layers, and adapt their connections through learning — enabling powerful tasks like vision, language, and speech.