✅ SUPER SHORT 10-LINE REVISION (Viva + Placements Ready) gen ai

 ✅ SUPER SHORT 10-LINE REVISION (Viva + Placements Ready)

1️⃣ Rule-Based AI → Fixed IF-THEN logic, no learning ability.
2️⃣ Needed improvement because real data is complex and dynamic.
3️⃣ RNN → Introduced sequential memory using hidden states.
4️⃣ Problem: RNN suffers from vanishing gradient (forgets long context).
5️⃣ LSTM → Added gates (forget/input/output) to manage long-term memory.
6️⃣ Improvement: Better handling of long sequences than RNN.
7️⃣ GRU → Simplified LSTM with fewer gates → faster & lighter model.
8️⃣ Limitation: RNN/LSTM/GRU process data step-by-step (slow training).
9️⃣ Transformer → Uses Self-Attention to process all tokens together.
🔟 Result: Parallel training + long context understanding → foundation of GPT, BERT, modern LLMs.

Rule-Based AI

   │

   ├─ Idea: IF–THEN rules

   ├─ 👍 Simple logic

   └─ ❌ No learning, not scalable

        ↓ (Need learning from data)

RNN (Recurrent Neural Network)

   │

   ├─ Idea: Sequential memory (hidden state)

   ├─ 👍 Understands sequences (text/time-series)

   └─ ❌ Vanishing gradient, poor long memory

        ↓ (Need better memory control)

LSTM (Long Short-Term Memory)

   │

   ├─ Idea: Gates → Forget | Input | Output

   ├─ 👍 Long-term dependency handling

   └─ ❌ Heavy & slow computation

        ↓ (Need simpler faster model)

GRU (Gated Recurrent Unit)

   │

   ├─ Idea: Simplified LSTM (Update + Reset gates)

   ├─ 👍 Faster, fewer parameters

   └─ ❌ Still sequential processing

        ↓ (Need parallel processing)

Transformer

   │

   ├─ Idea: Self-Attention (see all words at once)

   ├─ 👍 Parallel training + long context

   └─ 🚀 Base of GPT, BERT, modern LLMs