Introduction to Neural Networks for Beginners: Understanding the Brains Behind AI

📗 Chapter 4: Types of Neural Networks and Their Use Cases

Explore the Right Neural Network for the Right Job

🧠 Introduction

Neural networks come in many forms — some are built to analyze static images, others to handle speech, and some even generate new content like music and art. While the basic principles are shared, each type of neural network is optimized for a specific kind of data or task.

Choosing the right neural network architecture is crucial for building accurate, efficient, and scalable AI solutions.

In this chapter, we’ll explore:

• The major types of neural networks
• Their structure and specialties
• Real-world applications across industries
• Code examples for each type using Keras and PyTorch

📘 Section 1: Feedforward Neural Networks (FNNs)

🔹 Overview:

• The simplest type of neural network
• Data moves in one direction — from input to output
• No memory of past inputs

🔧 Use Cases:

💻 Code Example: FNN in Keras

python

from keras.models import Sequential

from keras.layers import Dense

model = Sequential()

model.add(Dense(64, input_dim=4, activation='relu'))

model.add(Dense(1, activation='sigmoid'))

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

📘 Section 2: Convolutional Neural Networks (CNNs)

🔹 Overview:

• Designed to process grid-like data, especially images
• Uses filters/kernels to extract features
• Detects edges, shapes, objects in image data

🧠 Architecture Components:

🔧 Use Cases:

💻 Code Example: CNN in Keras

python

from keras.models import Sequential

from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

model = Sequential()

model.add(Conv2D(32, (3,3), input_shape=(64,64,3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dense(1, activation='sigmoid'))

📘 Section 3: Recurrent Neural Networks (RNNs)

🔹 Overview:

• Designed for sequential data (e.g., time-series, text, audio)
• Has memory of previous inputs through loops
• Struggles with long sequences due to vanishing gradients

🔧 Use Cases:

💻 Code Example: Basic RNN in Keras

python

from keras.models import Sequential

from keras.layers import SimpleRNN, Dense

model = Sequential()

model.add(SimpleRNN(50, input_shape=(10, 1)))  # 10 time steps, 1 feature

model.add(Dense(1))

📘 Section 4: Long Short-Term Memory Networks (LSTM)

🔹 Overview:

• A type of RNN designed to remember longer sequences
• Uses gates (forget, input, output) to control memory

🔧 Use Cases:

💻 Code Example: LSTM in Keras

python

from keras.models import Sequential

from keras.layers import LSTM, Dense

model = Sequential()

model.add(LSTM(100, input_shape=(20, 1)))  # 20 time steps

model.add(Dense(1))

📘 Section 5: Generative Adversarial Networks (GANs)

🔹 Overview:

• Consist of two networks: Generator and Discriminator
• Generator tries to create fake data
• Discriminator tries to distinguish fake from real
• Improves through adversarial training

🔧 Use Cases:

💻 Pseudocode Example: GAN Setup

python

# Generator

generator = Sequential([

    Dense(128, activation='relu', input_dim=100),

    Dense(784, activation='sigmoid')

])

# Discriminator

discriminator = Sequential([

    Dense(128, activation='relu', input_dim=784),

    Dense(1, activation='sigmoid')

])

Note: Actual GANs require more complex training logic using adversarial loops.

📘 Section 6: Transformer Networks

🔹 Overview:

• Revolutionized NLP and sequence modeling
• Uses self-attention to relate different parts of input
• Powering models like GPT, BERT, ChatGPT

🔧 Use Cases:

🧠 Transformer Features:

• No recurrence or convolution
• Extremely parallelizable
• Scales well for huge datasets

📘 Section 7: Comparing Neural Network Types

✅ Chapter Summary Table