🔥 Excellent, Rajeev! Let’s begin our Day 0 — the official start of your Generative AI / LLM Engineering journey 🚀

This day ensures your system is ready for AI work, and that your Python foundations are AI-grade strong — fast, modern, and hands-on.
By the end of today, you’ll be able to run AI notebooks, understand model code, and move confidently into Deep Learning.

🧠 DAY 0 — Environment Setup + Python Refresher for AI

🎯 GOAL

Set up your full environment (Colab or local)
+
Master Python fundamentals (loops, functions, OOP, list comprehensions, generators)
so you’re fully ready to code neural networks & LLMs.

⚙️ PART 1 — Environment Setup for AI

Let’s make sure you have everything ready to run any AI project — from Hugging Face models to custom training.

🧩 Option 1: Easiest — Google Colab (Recommended for Now)

✅ No installation needed.
✅ GPU available free (or via Colab Pro).
✅ Great for running all notebooks up to fine-tuning.

👉 Steps:

Go to https://colab.research.google.com
Log in with your Google account
Click → “New Notebook”
In the top menu → Runtime → Change runtime type → Select:
- Hardware accelerator: GPU
- Runtime shape: Standard

Test your GPU:

import torch
print(torch.cuda.is_available())

If it returns True, ✅ GPU is active.

🧩 Option 2: Local Setup (For advanced / offline use)

You’ll need:

🐍 Python 3.10+
🧰 VS Code or Jupyter
⚙️ Install required packages:

pip install torch torchvision torchaudio
pip install transformers datasets sentencepiece accelerate
pip install numpy pandas matplotlib seaborn scikit-learn
pip install ipywidgets tqdm

(Optional for deployment & agents):

pip install fastapi uvicorn langchain chromadb openai

✅ Test installation:

import torch, transformers
print("Torch:", torch.__version__)
print("Transformers:", transformers.__version__)

🧩 PART 2 — Python Refresher for AI (Interactive + Practical)

Let’s revisit Python, but the AI way — fast, clean, and functional.

We’ll focus on the parts you’ll actually use for model building, data pipelines, and ML logic.

🧱 1. Variables & Data Types (AI context)

Every AI model’s input and parameters are numbers — Python represents them via int, float, list, tuple, dict, numpy array.

x = 42           # int
lr = 0.001       # float (learning rate)
layers = [64, 128, 256]   # list (NN architecture)
model_config = {'layers': 3, 'activation': 'relu'}  # dict (model params)

🔁 2. Loops & List Comprehensions (for data processing)

Used for iterating batches, generating tokens, or preprocessing data.

# Normal loop
squares = []
for i in range(5):
    squares.append(i**2)

# List comprehension (compact)
squares = [i**2 for i in range(5)]

Example — simulate text tokens:

sentence = "hello world this is AI"
tokens = [word.upper() for word in sentence.split()]
print(tokens)

⚙️ 3. Functions (used everywhere in ML)

Functions define operations — forward pass, activation, or loss computation.

def relu(x):
    return max(0, x)

print(relu(-5), relu(3))

Or with multiple parameters:

def forward_pass(x, w, b):
    return w*x + b

output = forward_pass(2.0, 0.5, 1.0)
print("Output:", output)

🧩 4. Lambda & Map/Filter (cleaner functional patterns)

Used for quick vectorized ops, like data cleaning or token filtering.

nums = [1, 2, 3, 4]
squares = list(map(lambda x: x**2, nums))
evens = list(filter(lambda x: x % 2 == 0, nums))

⚡ 5. Classes & OOP (Blueprint of Neural Networks)

All deep learning models are classes (in PyTorch, every model inherits from nn.Module).

class Neuron:
    def __init__(self, weight, bias):
        self.weight = weight
        self.bias = bias
    
    def forward(self, x):
        return self.weight * x + self.bias

n1 = Neuron(0.5, 1.0)
print("Output:", n1.forward(2))

This is exactly what happens in a neural network layer!

⚙️ 6. Generators (used in data pipelines)

Used to yield data batch-by-batch instead of storing all in memory.

def data_loader():
    for i in range(1, 6):
        yield f"Batch {i}"

for batch in data_loader():
    print(batch)

🔢 7. NumPy Refresher (foundation of tensors)

import numpy as np

arr = np.array([[1, 2], [3, 4]])
print("Shape:", arr.shape)
print("Mean:", np.mean(arr))
print("Dot product:", np.dot(arr[0], arr[1]))

This logic will map directly to torch tensors next.

🧠 8. Practice Exercise

👉 Try writing this:

Create a list of numbers 1–10
Write a function square_even_numbers() that returns only the squares of even numbers
Print the result

(You can share your code here — I’ll review it like your mentor.)

✅ SUMMARY: What You’ve Set Up Today

✔️ Installed / verified Python, Torch, and Hugging Face
✔️ Learned Python for ML (loops, functions, OOP, comprehensions, generators)
✔️ Ready for PyTorch, TensorFlow, and Transformer code
✔️ Understood how core Python maps directly to AI logic

🪄 Coming Next

👉 Day 1 — From Deep Learning → LLMs (The Evolution of Generative AI)
We’ll go from early neural nets → RNN → Transformers → GPT with visual intuition.

Would you like me to:
1️⃣ Give you a Colab notebook (Day 0) with all exercises & runnable code
or
2️⃣ Continue straight to Day 1: Evolution from Deep Learning to LLMs (with visuals and hands-on demos)?

ABC TO XYZ OF AI

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