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Module 4: Advanced AI Architectures – Images, Text & Attention

Duration: Week 7-9
Difficulty: Intermediate-Advanced
Prerequisites: Module 3 completed

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🎯 What You’ll Learn

Different problems need different tools. You wouldn’t use a hammer to cut paper! Similarly, different AI architectures are designed for different tasks.

CNNs: For images (recognizing cats, detecting objects)
RNNs: For sequences (text, time series, music)
Transformers: For everything (the modern breakthrough!)

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📷 Part 1: CNNs (Convolutional Neural Networks)

The Problem with Regular Networks for Images

A 256×256 color image = 196,608 numbers!
A regular network would need millions of connections → Too slow, too much memory

The CNN Solution – Look for Patterns Locally

Think about how YOU recognize a cat:
1. First notice: edges, curves, corners
2. Then combine: eyes, ears, whiskers
3. Finally: “That’s a cat!”

CNNs do the same thing!

How CNNs Work

1. Convolution Layers – Pattern Detectors

Imagine sliding a small magnifying glass across the image:

The magnifying glass is a "filter" (3x3 or 5x5 pixels)
It looks for specific patterns (edges, colors, textures)
Multiple filters find different patterns

2. Pooling Layers – Shrinking While Keeping Important Info

MaxPooling: In each 2x2 region, keep only the biggest number
Why: Reduces size, keeps important features
Like: Summarizing a story - keep main points, drop details

Project: Build Your Own CNN

Dataset: CIFAR-10 (60,000 tiny images, 10 categories)
Architecture: 3 conv layers, 2 pooling, 1 dense
Goal: 70%+ accuracy
Training time: 30 minutes on CPU!

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📝 Part 2: RNNs (Recurrent Neural Networks)

The Problem: Understanding Order Matters

“Dog bites man” ≠ “Man bites dog”
Regular networks don’t understand sequence/order

The RNN Solution – Networks with Memory

Analogy: Reading a book

You remember previous chapters while reading current one
Each word makes sense because of previous words
That’s how RNNs work!

LSTM (Long Short-Term Memory)

Problem with basic RNN: Forgets long-term info
Solution: LSTM has special “memory cells”

Think of it like:

Short-term memory: What you just read
Long-term memory: Important plot points
LSTM decides: What to remember, what to forget

Project: Text Generator

Dataset: Shakespeare’s plays
Model: Character-level LSTM
Input: “To be or not to”
Output: “be, that is the question”
Training: 1 hour on CPU

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🤖 Part 3: Transformers – The Modern Breakthrough

The Revolution: Attention Mechanism

Old way (RNN): Process words one by one, left to right
New way (Transformer): Look at ALL words at once, focus on important ones

Attention Explained Simply

When you read “The cat sat on the mat because it was tired”

Question: What does “it” refer to?
Your brain: Pays ATTENTION to “cat” (not “mat”)
Transformers do the same thing mathematically!

How Attention Works

For each word:
1. Compare it with every other word
2. Calculate "attention scores" (how related are they?)
3. Focus more on highly related words
4. Combine information weighted by attention

Project: Mini-Transformer

Build: 2-layer transformer
Dataset: TinyStories (simple children’s stories)
Parameters: 10-20 million (fits in your RAM!)
Result: Generates coherent short sentences
Training: 2-3 hours on CPU

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⚡ ISL Optimizations

1. Depthwise Separable Convolutions (MobileNet)

Regular convolution: Expensive
Depthwise separable: 9x faster, same accuracy!
Perfect for laptops and phones

2. Attention Memory Optimization

Problem: Attention matrix is N×N
Solution: Use shorter sequences (512 instead of 2048)
Result: 16x less memory!

3. Model Compression

Quantization – Use Smaller Numbers

Normal: 32-bit (4 bytes)
Quantized: 8-bit (1 byte)
Result: 4x smaller, 2-4x faster!

Pruning – Remove Unnecessary Connections

Train → Identify weak connections → Remove → Retrain
Result: 50-90% fewer parameters!

Knowledge Distillation – Teacher-Student Learning

Big model (teacher): Accurate but slow
Small model (student): Fast but less accurate
Process: Student learns to mimic teacher
Result: Small model with big model's performance!

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