Artificial Intelligence with Generative-AI

INTRODUCTION TO ARTIFICIAL INTELLIGENCE

• Introduction to Artificial Intelligence
• Life Cycle of Artificial Intelligence
• Difference between AI vs ML vs DL vs Gen AI
• Introduction to Generative AI
• Python Programming
• Pandas
• What is Machine Learning
• Supervised Learning / Unsupervised Learning /Reinforcement Learning

Deep Learning:

• Introduction to Deep Learning
• Introduction ANN
• Understanding of Tensorflow and Keras
• Forward Propagation
• Back Propagation
• Optimizer
• Regularization
• Introduction to CNN
• Implementation of CNN

Natural Language Processing

• Introduction to NLP
• History of NLP
• Embedding
• Word2Vec
• Bag of words
• TF-IDF
• Data Augmentation
• Vectorization
• Part-of-Speech
• Sentiment Analysis
• Implementation of RNN / LSTM / GRU

Large Language Models

• Introduction to Encoder / Decoder
• Understanding Self Attention
• What are Transformers
• Types of Transformers
• Implementation of Transformers
• What is LLMs
• Types of LLMs
• Pre-training
• Fine-training
• LLM Application

Working with Hugging Face Ecosystem

• Introduction to Hugging Face Ecosystem
• Hugging Face Transformers Library
• Exploring Hugging Face Models and Tokenizers.
• Performing Sentiment Analysis using pre-trained model
• Introducing to Trainer API
• Using Hugging Face Model hub to share models.
• Multi-Lingual and cross – lingual transfer learning.
• Using pipelines for different tasks (text generation, named entity recognition,)
• Integrating Hugging Face models with web application

Working with LangChain

• Introduction to the LangChain framework
• Understanding the purpose and core components of LangChain Framework
• LangChain Setup and necessary dependencies
• Basic configuration and setup for development
• Step-by-step guide to creating a simple application using LangChain Framework
• Detailed walkthroughs of real-world applications built with LangChain

Meta’s LLaMA API

• Introduction of LLaMA .
• Comparison with other large language models like GPT-3 and GPT-4.
• Key features and capabilities of LLaMA
• Understanding the Model Architecture of LLaMA.
• Discussion on model sizes and capabilities.
• Environment setup: Installing necessary libraries and tools machines or cloud platforms (Meta LLaMa) .
• Intro to the architecture of LLaMA models
• Accessing LLaMA models: Overview of the download process and setup on local
• Understanding the differences between LLaMA model variants (8B, 13B, 30B, and 70B parameters)
• Implementing text generation using LLaMA         

Open AI API

• Intro To Open Ai Working with Open AI API
• Utilizing OpenAI APIs
• Setting up and authenticating API usage.
• Practical exercises using GPT-3/GPT-4 for text generation.

Prompt Engineering and Working With LLM

• Intro to Prompt Engineering
• LLM with Prompt Engineering
• Introduction to GPT models.
• Understanding how GPT-3 and GPT-4 work
• Training on popular LLMs like GPT (Generative Pre-trained Transformer).
• Practical applications of LLMs in generating text, code, and more

Working with Google Gemini API

• Getting Started with Gemini
• How to obtain an API key for Gemini.
• Overview of the Gemini API and accessing its features.
• Detailed exploration of different Gemini models.
• Selecting and initializing the right model for specific tasks.
• Step-by-step project to create an AI-powered chatbot using Gemini.

Building Gen AI Apps Using LangChain

• Introduction to the LangChain framework
• Understanding the purpose and core components of LangChain Framework
• LangChain Setup and necessary dependencies
• Basic configuration and setup for development
• Step-by-step guide to creating a simple application using LangChain Framework
• Detailed walkthroughs of real-world applications built with LangChain

Introduction To Stable Diffusion and Retrieval-Augmented Generation

• Introduction to Stable Diffusion
• Fundamentals of Diffusion Models
• Application of Stable Diffusion
• Modifying image attributes and styles using prompt engineering
• Parameters of image generation: seeds, prompts, and steps explained
• Tool For Stable Diffusion
• Fine-tuning and training Stable Diffusion on custom datasets
• Introduction to Retrieval-Augmented Generation
• Building applications using Retrieval-Augmented Generation

FINAL: Project Implementation