Perfect, Rajeev 🙌
Welcome officially to Day 1 of your Generative AI & LLM Engineering Course —
this is where the real journey begins 🚀

You’ll understand how Artificial Intelligence evolved from simple neural networks to ChatGPT-like systems, through the major breakthroughs:
Deep Learning → Sequence Models → Transformers → GPT → LLMs.

Let’s go step by step, visually, intuitively, and practically.

🌎 DAY 1 — From Deep Learning → LLMs (The Evolution of Generative AI)

🧠 1. The Big Picture: What is AI, ML, Deep Learning?

🔍 Analogy:

• AI = “Human brain capabilities in machines”
• ML = “Teaching the machine to learn patterns by example”
• DL = “Letting the machine learn representations from data directly”

🧩 2. From Traditional ML → Neural Networks

💡 Before Deep Learning:

We manually created features like:

• Word count
• Length of sentence
• Color intensity in images

→ Models like Logistic Regression or SVM learned from these features.

Problem: 🤕 They couldn’t understand raw data like images, sound, or language directly.

⚙️ Neural Networks to the Rescue

A Neural Network learns these features automatically.

Visual:

Input → [Hidden Layer 1] → [Hidden Layer 2] → Output

Each layer transforms data a bit — like a human brain with neurons.

Example:

• Input: [pixel values of an image]
• Hidden layers: learn edges, shapes, objects
• Output: “Cat” 🐱

🧮 How a single neuron works

Output = (Input × Weight) + Bias
If Output > 0 → Activate (like ReLU)

Example:

x = 2.0      # input
w = 0.5      # weight
b = 1.0      # bias
y = w*x + b  # 2.0

Each neuron does this simple math — billions of times in deep networks!

🧠 3. Deep Learning Era (2012–2017)

Breakthroughs came when networks got deeper (more layers) and trained on GPUs.
That’s why we call it Deep Learning.

💥 Key Architectures:

🔄 4. Sequence Models — RNNs & LSTMs

Before Transformers, we used Recurrent Neural Networks for text.

They processed text word by word — maintaining a hidden memory.

Visual:

[Word1] → [Word2] → [Word3] → ... → Output

Each word updates the network’s hidden state.

🧩 Problem with RNNs:

• Hard to remember long sentences (vanishing gradient)
• Slow (can’t process in parallel)
• Difficult to handle long contexts

💬 Example:

“The boy who owned the dog … was happy.”

RNNs often forget “boy” by the time they reach “was”.

⚡ 5. The Breakthrough: Transformers (2017)

Paper: “Attention Is All You Need” — Vaswani et al., 2017

Transformers solved RNN’s problems using Attention Mechanism.
Instead of remembering everything sequentially, they look at all words at once — and learn which parts to focus on.

⚙️ Visual Intuition

Sentence:

“The cat sat on the mat.”

When predicting “mat”, the model attends to:

• “cat” 🐱
• “sat” 🪑
• “on” 🪶

more than to irrelevant words like “the”.

🧭 Self-Attention Mechanism

It computes how much each word relates to others.

Mathematically:

Attention(Q, K, V) = softmax(QKᵀ / √dₖ) * V

But intuitively:

“When I process the word ‘mat’, which other words should I pay attention to?”

💡 Example Demo

Let’s simulate a mini “attention” in code:

import numpy as np

words = ["I", "love", "AI"]
attention_scores = np.array([
    [1.0, 0.2, 0.1],   # I attends to itself mostly
    [0.2, 1.0, 0.6],   # love attends to "AI" too
    [0.1, 0.6, 1.0]    # AI attends to "love"
])

print("Attention Map:")
print(attention_scores)

This gives a relationship matrix between tokens — like a brain heatmap 🧠🔥.

🏗️ 6. Transformer Architecture Overview

Each transformer block has:

Input Embedding
 ↓
Multi-Head Self Attention
 ↓
Feed Forward Layer
 ↓
Residual + Layer Normalization
 ↓
Output

Multiple blocks stacked together → large model.

🔍 Encoder vs Decoder

🤖 7. GPT: Generative Pre-trained Transformer

GPT = Decoder-only Transformer that learns to predict the next word.

🧠 Training Objective:

Given words so far → Predict the next one

Example:

Input: “The capital of France is”
Output: “Paris”

This is called Next Token Prediction — the foundation of ChatGPT!

🔁 How GPT “thinks”

1. Take your text → tokenize → embeddings
2. Apply multiple self-attention layers
3. Predict next token
4. Add it back and repeat (autoregressive generation)

So GPT generates one token at a time — word by word, like human thought.

🧬 8. LLMs = Scaling Up GPT

The big realization:

The same architecture, but trained on huge data + massive GPUs = intelligence emerges.

🧠 9. Why Transformers Beat Everything

✅ Process sequences in parallel
✅ Handle long context
✅ Understand relationships via attention
✅ Scalable (just add layers & data)
✅ Transferable to any domain — text, image, code, sound

🧩 10. Hands-On Mini Project: Build a Tiny Transformer

Let’s build a Mini-GPT-like model that predicts the next character:

import torch
import torch.nn as nn
import torch.nn.functional as F

# Sample data
text = "hello world"
chars = sorted(list(set(text)))
vocab_size = len(chars)

# Mappings
stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for ch, i in stoi.items()}

# Encode text
data = torch.tensor([stoi[c] for c in text], dtype=torch.long)

# Tiny model
class TinyTransformer(nn.Module):
    def __init__(self, vocab_size, emb_dim=8):
        super().__init__()
        self.embedding = nn.Embedding(vocab_size, emb_dim)
        self.fc = nn.Linear(emb_dim, vocab_size)
    
    def forward(self, x):
        emb = self.embedding(x)
        out = self.fc(emb)
        return out

model = TinyTransformer(vocab_size)
out = model(data)
print(out.shape)

This shows the core principle:
Input characters → embeddings → transform → predict next token.

🧩 11. The Modern Stack

🧭 Summary: Evolution Map

ML (SVM, RF)
   ↓
Neural Networks
   ↓
CNN → RNN/LSTM
   ↓
Transformer (2017)
   ↓
GPT (Generative Transformer)
   ↓
LLMs + RLHF + Agents (ChatGPT, Claude, Gemini)

🎯 What You Learned Today

✅ How AI → ML → DL evolved
✅ How Neural Nets & RNNs led to Transformers
✅ How GPT works (next-token prediction)
✅ Built your first mini-transformer
✅ Understood why attention is revolutionary

🔮 Coming Next

👉 Day 2 — Tokenization & Embeddings
You’ll learn how words turn into numbers,
visualize embeddings in 2D space,
and understand how language meaning is encoded mathematically.

Would you like me to:
1️⃣ Create an interactive Colab notebook for this full lesson (Day 1, with code + visuals + exercises),
or
2️⃣ Move on directly to Day 2 — Tokenization & Embeddings (the next building block of LLMs)?

# %% [markdown]
# Day 1 — From Deep Learning → LLMs

This interactive Colab-ready notebook teaches the evolution from classic neural networks → RNNs → Transformers → GPT, with runnable code, visualizations, and exercises.

**How to use:**
- Open Google Colab: https://colab.research.google.com
- Create a new notebook and paste each cell below into separate cells (Markdown cells for the markdown blocks, Code cells for the code blocks).

---

# %% [markdown]
## 1. Simple Neuron & Feedforward Intuition

A single neuron: output = w * x + b. We'll visualize a simple neuron response.

---

# %%
# Simple neuron in code
import numpy as np
import matplotlib.pyplot as plt

def neuron(x, w=0.8, b=0.2):
    return w * x + b

xs = np.linspace(-5, 5, 200)
ys = neuron(xs)

plt.figure(figsize=(6,4))
plt.plot(xs, ys)
plt.title('Single neuron: y = w*x + b')
plt.xlabel('x')
plt.ylabel('y')
plt.grid(True)
plt.show()

# Print a few values
print('sample outputs (x->y):', list(zip(xs[::50].round(2), ys[::50].round(2))))

# %% [markdown]
## 2. Recurrent intuition (RNN-like): processing a sequence

We'll show how an RNN would process a sequence step by step (hidden state update). This is purely illustrative.

---

# %%
# Simple RNN step simulation
import numpy as np

def rnn_step(x_t, h_prev, Wx=0.5, Wh=0.8, b=0.0):
    # h_t = tanh(Wx * x_t + Wh * h_prev + b)
    return np.tanh(Wx * x_t + Wh * h_prev + b)

sequence = [1.0, 0.5, -0.2, 0.8, 0.0]
h = 0.0
print('t\tx_t\th_t')
for t, x in enumerate(sequence):
    h = rnn_step(x, h)
    print(f'{t}\t{x}\t{h:.4f}')

# %% [markdown]
## 3. Attention: visual intuition and tiny attention map

Self-attention computes pairwise scores between tokens. We'll create a simple attention matrix for a short sentence and visualize it.

---

# %%
# Tiny attention demonstration
import numpy as np
import matplotlib.pyplot as plt

words = ['The', 'cat', 'sat', 'on', 'the', 'mat']
N = len(words)

# Create a synthetic "attention" matrix where nouns attend to each other more, etc.
attn = np.full((N, N), 0.1)
# boost some relations
attn[1, 2] = 0.6  # 'cat' attends to 'sat'
attn[1, 5] = 0.7  # 'cat' attends to 'mat'
attn[2, 1] = 0.5
attn[5, 1] = 0.6

# normalize rows like softmax (simple normalization)
attn = attn / attn.sum(axis=1, keepdims=True)

plt.figure(figsize=(6,5))
plt.imshow(attn, cmap='Blues')
plt.colorbar()
plt.xticks(range(N), words, rotation=45)
plt.yticks(range(N), words)
plt.title('Toy Self-Attention Map')
plt.show()

print('Attention matrix (rows sum to 1):\n', np.round(attn, 3))

# %% [markdown]
## 4. Implementing a tiny attention layer (vectorized)

This code creates Query, Key, Value from small embeddings and computes scaled dot-product attention.

---

# %%
import torch
import torch.nn.functional as F

# toy embeddings (N tokens x D embedding)
emb = torch.tensor([[1.0,0.0,0.0],
                    [0.9,0.1,0.0],
                    [0.2,0.8,0.0],
                    [0.0,1.0,0.0]], dtype=torch.float32)  # 4 tokens, D=3

# linear projections (for demo we reuse small random matrices)
D = emb.shape[1]
Wq = torch.randn((D, D)) * 0.5
Wk = torch.randn((D, D)) * 0.5
Wv = torch.randn((D, D)) * 0.5

Q = emb @ Wq
K = emb @ Wk
V = emb @ Wv

scores = Q @ K.T / (D ** 0.5)
attn = F.softmax(scores, dim=-1)

print('Scores shape:', scores.shape)
print('Attention weights (row sums):', attn.sum(dim=1))

out = attn @ V
print('Output shape:', out.shape)

# Visualize attention weights
plt.figure(figsize=(5,4))
plt.imshow(attn.detach().numpy(), cmap='viridis')
plt.colorbar()
plt.title('Scaled Dot-Product Attention Weights (toy)')
plt.xlabel('Key Token')
plt.ylabel('Query Token')
plt.show()

# %% [markdown]
## 5. Tiny Transformer Block (embedding -> attention -> feedforward)

We'll implement a minimal transformer block using PyTorch modules. This is a *very* compact educational version.

---

# %%
import torch
import torch.nn as nn

class TinyBlock(nn.Module):
    def __init__(self, d_model=16, nhead=2, dim_ff=32):
        super().__init__()
        self.attn = nn.MultiheadAttention(embed_dim=d_model, num_heads=nhead, batch_first=True)
        self.ff = nn.Sequential(
            nn.Linear(d_model, dim_ff),
            nn.ReLU(),
            nn.Linear(dim_ff, d_model)
        )
        self.ln1 = nn.LayerNorm(d_model)
        self.ln2 = nn.LayerNorm(d_model)

    def forward(self, x):
        # x: batch x seq_len x d_model
        attn_out, _ = self.attn(x, x, x)
        x = self.ln1(x + attn_out)
        ff_out = self.ff(x)
        x = self.ln2(x + ff_out)
        return x

# quick smoke test
blk = TinyBlock(d_model=16, nhead=2)
x = torch.randn((1, 6, 16))
out = blk(x)
print('Block output shape:', out.shape)

# %% [markdown]
## 6. Mini-GPT: Char-level next-token predictor (very small)

We'll build a tiny model that predicts the next character from a short string. This demonstrates the autoregressive flow (tokenize -> embed -> transformer -> predict -> sample).

---

# %%
import torch
import torch.nn as nn
import torch.optim as optim

text = "hello world"
chars = sorted(list(set(text)))
stoi = {ch:i for i,ch in enumerate(chars)}
itos = {i:ch for ch,i in stoi.items()}
vocab_size = len(chars)

# dataset
data = torch.tensor([stoi[c] for c in text], dtype=torch.long)
inputs = data[:-1].unsqueeze(0)  # shape (1, seq_len)
targets = data[1:].unsqueeze(0)

class MiniGPT(nn.Module):
    def __init__(self, vocab_size, d_model=32):
        super().__init__()
        self.embed = nn.Embedding(vocab_size, d_model)
        self.pos = nn.Parameter(torch.zeros(1, 50, d_model))  # small positional
        self.block = TinyBlock(d_model=d_model, nhead=4, dim_ff=64)
        self.fc = nn.Linear(d_model, vocab_size)

    def forward(self, x):
        b, seq = x.shape
        emb = self.embed(x) + self.pos[:, :seq, :]
        out = self.block(emb)
        logits = self.fc(out)
        return logits

# init
model = MiniGPT(vocab_size)
loss_fn = nn.CrossEntropyLoss()
opt = optim.Adam(model.parameters(), lr=1e-2)

# train for a few steps
model.train()
for epoch in range(120):
    opt.zero_grad()
    logits = model(inputs)  # (1, seq, vocab)
    loss = loss_fn(logits.view(-1, vocab_size), targets.view(-1))
    loss.backward()
    opt.step()
    if epoch % 30 == 0:
        print(f'Epoch {epoch} Loss: {loss.item():.4f}')

# sampling function
model.eval()
with torch.no_grad():
    x = torch.tensor([[stoi['h']]])  # start token 'h'
    generated = ['h']
    for _ in range(10):
        logits = model(x)
        last_logits = logits[0, -1]
        probs = torch.softmax(last_logits, dim=-1)
        idx = torch.multinomial(probs, num_samples=1).item()
        ch = itos[idx]
        generated.append(ch)
        x = torch.cat([x, torch.tensor([[idx]])], dim=1)

print('Generated:', ''.join(generated))

# %% [markdown]
## 7. Exercises (Try these yourself)

1. Modify the `text` variable to another short phrase (e.g., "good day") and retrain the MiniGPT. See if it learns to generate similar text.
2. Change `d_model` and `nhead` in `TinyBlock`. How does training stability and speed change?
3. Visualize attention weights inside `TinyBlock` by modifying the block to return the attention matrix and plotting it for a sample input.
4. Implement a simple tokenizer that converts words to integer ids for a small corpus and build a word-level MiniGPT.

---

# %% [markdown]
## 8. Summary & Next Steps

You just:
- Visualized a neuron and an RNN hidden update
- Built a toy attention map and tiny attention module
- Implemented a minimal transformer block and a mini autoregressive model

**Next:** Day 2 — Tokenization & Embeddings (we'll visualize embeddings in 2D and learn fast tokenization techniques).


# %% [markdown]
# End of notebook

# Tips:
# - If you run on Colab and want GPU acceleration, go to Runtime -> Change runtime type -> GPU.
# - This notebook is intentionally small so it runs quickly even on CPU. Feel free to scale up sequences and model sizes if you have a GPU.

Pages: 1 2 3 4 5 6 7 8 9