Generative AI Masterclass

Master Generative AI from foundations to deployment. Learn LLMs, RAG, image generation, prompt engineering, and ethical AI practices.

Lesson 1: Introduction to Generative Models

Learn about Introduction to Generative Models in this comprehensive lesson.

// Module: Generative AI Masterclass -> Introduction to Generative Models

Lesson 2: Python Environment Setup for GenAI

Learn about Python Environment Setup for GenAI in this comprehensive lesson.

// Module: Generative AI Masterclass -> Python Environment Setup for GenAI

Lesson 3: Mathematical Core: Probability & Statistics

Learn about Mathematical Core: Probability & Statistics in this comprehensive lesson.

// Module: Generative AI Masterclass -> Mathematical Core: Probability & Statistics

Lesson 4: The Transformer Architecture Deep Dive

Learn about The Transformer Architecture Deep Dive in this comprehensive lesson.

// Module: Generative AI Masterclass -> The Transformer Architecture Deep Dive

Lesson 5: Pre-training and Fine-tuning Paradigms

Learn about Pre-training and Fine-tuning Paradigms in this comprehensive lesson.

// Module: Generative AI Masterclass -> Pre-training and Fine-tuning Paradigms

Lesson 6: Working with Open Source Models

Learn about Working with Open Source Models in this comprehensive lesson.

// Module: Generative AI Masterclass -> Working with Open Source Models

Lesson 7: Prompt Engineering Advanced Techniques

Learn about Prompt Engineering Advanced Techniques in this comprehensive lesson.

// Module: Generative AI Masterclass -> Prompt Engineering Advanced Techniques

Lesson 8: Retrieval Augmented Generation (RAG)

Learn about Retrieval Augmented Generation (RAG) in this comprehensive lesson.

// Module: Generative AI Masterclass -> Retrieval Augmented Generation (RAG)

Lesson 9: Building Applications with LangChain

Learn about Building Applications with LangChain in this comprehensive lesson.

// Module: Generative AI Masterclass -> Building Applications with LangChain

Lesson 10: Diffusion Models Explained

Learn about Diffusion Models Explained in this comprehensive lesson.

// Module: Generative AI Masterclass -> Diffusion Models Explained

Generative AI Masterclass

Setting Up a Python Environment for Generative AI

This introductory module covers the foundational steps for any AI developer: isolating dependencies using Python's venv, installing essential libraries like PyTorch and Hugging Face Transformers, and executing a verification script to check for GPU (CUDA) availability.

Step 1: intro

Lesson 1: Environment Setup. Before we generate art or text, we need a stable Python playground.

Code / Terminal Command:

# Terminal Check
$ python --version

Step 2: venv create

We create a Virtual Environment to isolate our GenAI libraries from the rest of the system.

Code / Terminal Command:

# Create virtual environment
$ python -m venv genai_env

Step 3: activate

Now we activate it. Notice the visuals: our context shifts into the isolated 'genai_env' box.

Code / Terminal Command:

# Activate environment (Linux/Mac)
$ source genai_env/bin/activate

Step 4: install libs

The core stack: PyTorch for tensor math, and Transformers (Hugging Face) for the models.

Code / Terminal Command:

# Install dependencies
$ pip install torch transformers numpy

Step 5: script start

Let's create `main.py`. First, we import the libraries to verify they are accessible.

Code / Terminal Command:

import torch
from transformers import pipeline

# Check for GPU availability
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {device}")

Step 6: pipeline code

We'll add a simple 'pipeline' test. This downloads a tiny model to prove everything works.

Code / Terminal Command:

import torch
from transformers import pipeline

# Check for GPU availability
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Using device: {device}")

# Initialize a simple sentiment analysis pipeline
classifier = pipeline('sentiment-analysis')
result = classifier('Generative AI is amazing!')

print(result)

Step 7: execution

Back to the terminal. Run the script. If we see the tensor output, we are ready for Deep Learning.

Code / Terminal Command:

# Run the verification script
$ python main.py

Step 8: outro

Environment secure. Next lesson: We dive into the Mathematical Core—Probability & Statistics.

Code / Terminal Command:

# NEXT LESSON:
# 1. Tensors & Matrix Mult
# 2. Probability Distributions
# 3. Softmax Functions

GenAI 101: Environment Setup • Step 1 / 8

terminal

main.py

requirements.txt

Guide:Lesson 1: Environment Setup. Before we generate art or text, we need a stable Python playground.

Generative AI Masterclass

Duration: 20 hours

Lessons: 15

(4.9)

Master Generative AI from foundations to deployment. Learn LLMs, RAG, image generation, prompt engineering, and ethical AI practices.

Curriculum

Introduction to Generative Models
Python Environment Setup for GenAI
Mathematical Core: Probability & Statistics
The Transformer Architecture Deep Dive
Pre-training and Fine-tuning Paradigms
Working with Open Source Models
Prompt Engineering Advanced Techniques
Retrieval Augmented Generation (RAG)
Building Applications with LangChain
Diffusion Models Explained