Neural Networks Basics
Neural Networks are the foundation of Deep Learning. They are inspired by the
structure and functioning of the human brain. Just like our brain has billions
of interconnected neurons, a neural network consists of artificial neurons
connected in layers. These neurons learn patterns from data and make predictions.
In this chapter, you will learn what a neuron is, how layers work, what weights
and biases are, how neural networks learn, and how these ideas connect to
real-life examples like face recognition, Google Photos, ChatGPT, handwriting
detection, and fraud detection.
✅ What is a Neural Network?
A neural network is a system of algorithms designed to recognize patterns. It works
by passing data through a series of connected nodes (artificial neurons). Each neuron
processes information and passes it to the next. With enough data, the network learns
to make intelligent decisions.
A neural network usually has three main parts:
- Input Layer
- Hidden Layers (one or more)
- Output Layer
These layers work together to transform raw data into useful predictions.
📌 Real-Life Example: How Your Brain Recognizes a Friend
When you see a friend from far away, your brain instantly recognizes them.
How? It uses:
- Face shape
- Eyes
- Nose
- Mouth
- Hair style
Your brain has learned these features through repeated exposure.
A neural network does the same—it learns from examples.
📌 What is an Artificial Neuron?
An artificial neuron (also called a Node or Perceptron)
mimics a biological neuron. It receives one or more inputs, applies weights, adds a bias,
and sends an output through an activation function.
Mathematically, a neuron performs:
// simple neuron operation
output = activation( w1*x1 + w2*x2 + ... + wn*xn + bias );
Here:
- xi = input
- wi = weight
- bias = additional adjustment
- activation() = activation function
Everything you learn in later chapters—Perceptrons, MLP, Backpropagation—builds on this.
📌 Real-Life Example: Detecting Spam Emails
Suppose a neuron receives inputs like:
- Number of links
- Suspicious keywords
- Sender’s reputation
- Attachments present
Using weighted importance, the neuron predicts:
Is this email spam or not?
With training, the neural network becomes extremely accurate.
📌 Understanding Weights and Biases
Weights and biases control how important each input is.
They are the memory of a neural network.
Example: To identify a cat in an image, a network might learn:
- Weight for “whiskers” = high
- Weight for “ears” = high
- Weight for “fur texture” = medium
These weights adjust during training through Backpropagation (explained later).
📌 Real-Life Example: Netflix Learning Your Taste
Netflix learns your likes using weights:
- If you watch action movies → action_weight increases
- If you skip romance movies → romance_weight decreases
- If you binge comedy → comedy_weight increases
Over time, the system becomes personalized for you.
📌 Types of Neural Networks
Although deep learning has many architectures, the basic ones include:
- Feedforward Neural Network (FNN)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
In this chapter, we focus on the basic feedforward structure.
📌 Feedforward Neural Network (FNN)
An FNN moves data in one direction: Input → Hidden → Output.
Example: Predicting house prices using:
- Number of rooms
- Location
- Square feet
- Age of building
Each hidden layer transforms data into more meaningful patterns.
📌 Real-Life Example: Handwriting Recognition
When you write digits on a smartphone screen, the system instantly recognizes them.
This is done through a neural network trained on thousands of example digits.
Hidden layers learn:
- Edges
- Curves
- Line thickness
- Shape patterns
Finally, the output layer predicts the number (0 to 9).
📌 What is a Hidden Layer?
Hidden layers are where the “magic” happens.
They extract meaningful features from raw inputs.
Example: In image recognition:
- 1st hidden layer → detects edges
- 2nd hidden layer → detects shapes
- 3rd hidden layer → detects facial features
The deeper the network, the more powerful the extraction.
📌 Real-Life Example: Google Lens
Google Lens can:
- Identify objects
- Translate text instantly
- Recognize plants, animals, and monuments
All of this is possible because hidden layers extract features step-by-step.
📌 Why Do We Need Activation Functions?
Without activation functions, a neural network is just a linear system—unable to
learn complex patterns.
Activations introduce non-linearity, allowing the network to learn:
- Images
- Speech
- Text
- Emotion
- Noise patterns
The next chapter covers activation functions in detail.
📌 How Neural Networks Learn (Overview)
Neural networks learn through a process called training.
Training involves:
- Feeding data into the network
- Calculating prediction
- Comparing with correct answer
- Adjusting weights and biases
The weight adjustment step is done using Backpropagation and
Gradient Descent (covered in Chapter 5).
📌 Real-Life Example: Google Maps Traffic Prediction
Google Maps predicts traffic using millions of data points:
- Movement of phones
- Historical traffic patterns
- Road conditions
The neural network learns:
- When traffic usually occurs
- Which roads are slow
- How weather impacts speed
Then it gives accurate travel time predictions.
📌 Underfitting and Overfitting (Basic Intro)
Neural networks must balance learning:
- Underfitting: The network is too simple; it doesn’t learn enough.
- Overfitting: The network memorizes data instead of learning patterns.
This is why we split data into:
- Training Data
- Validation Data
- Testing Data
📌 Summary of This Chapter
In this chapter, you learned the basic building blocks of neural networks, including
neurons, layers, weights, biases, and how the network learns. You also explored real-life
examples like spam detection, face recognition, Google Lens, and Netflix recommendations.
These fundamentals will help you understand the more advanced topics in the next chapters:
Perceptron, Activation Functions, Forward & Backpropagation, and TensorFlow/Keras.
