Course curriculum

  • 1

    Module 1: Introduction to Large Language Models

    • LLM Overview

    • Evolution of LLMs

    • Capabilities and Limitations of LLMs

    • Applications and use cases of LLMs
  • 2

    Module 2: Core LLM Technologies

    • Tokenization, Vectors and Embeddings

    • Attention Mechanism and its variants

    • Introduction to Transformer Architecture

    • Creating Custom Language Models

    • Transfer Learning in NLP

    • Evaluation Metrics for LLMs: BLEU, ROUGE, Perplexity

    • Introduction to Hugging Face Transformers library

    • Overview of llama2 and Gemma

    • Fine Tuning Gemma Model
  • 3

    Module 3: Advanced LLM Techniques

    • Overview of popular LLMs: GPT-3/4, BERT, T5

    • Fine-tuning pre-trained models for specific tasks

    • BERT and its variants: RoBERTa, DistilBERT

    • GPT and its applications in text generation

    • Exploring other models: T5, XLNet, ELECTRA

    • Building conversational agents and chatbots - Part 1

    • Building conversational agents and chatbots - Part 2

    • Creative applications: text generation, storytelling - Part 1

    • Creative applications: text generation, storytelling - Part 2

    • Ethical considerations and bias mitigation in LLMs
  • 4

    Module 4: Computer Vision

    • Computer Vision (CV)

    • Introduction to Neural Networks

    • CNN from Scratch

    • CNN using Tensorflow
  • 5

    Module 5: Audio/Video Coding

    • Basics of audio signal processing

    • Feature extraction: MFCCs, Spectrograms - Part 1

    • Feature extraction: MFCCs, Spectrograms - Part 2

    • Audio classification and speech recognition - Part 1

    • Audio classification and speech recognition - Part 2

    • Basics of video signal processing

    • Frame extraction and video feature analysis - Part 1

    • Frame extraction and video feature analysis - Part 2
  • 6

    Module 6: LLM Frameworks and Tools

    • LangChain - Langchain for Conversational AI Applications

    • LangChain - Deploying Language Model APIs with Langchain

    • LangChain - Langchain for RAG Workflows

    • Ollama - Overview of Ollama for conversational AI

    • Ollama - Developing and deploying conversational agents with Ollama
  • 7

    Module 7: Practical Projects

    • Project 1: Text Classification Model

    • Bert_text_classification

    • Bert_text_classification_app

    • Data preparation and preprocessing

    • Text Generation Model

    • Project 2- Text Generation Model

    • Evaluation and fine-tuning

    • Evaluation and fine-tuning

    • Project3 - Designing a conversational agent architecture

    • ConversationalAgent

    • ConversationalAgent_App

    • Conversational_agent_openAI

    • Conversational_agent_openAI_app

    • ConversationalAgent_LangChain

    • ConversationalAgent_Ollama

    • ConversationalAgent_HuggingFace
  • 8

    Module 8: Deployment and MLOps

    • Introduction to MLOps concepts and practices

    • Continuous Integration and Continuous Deployment

    • Monitoring model performance in production

    • Handling model drift and retraining

    • Automated model testing and validation

    • Optimizing LLMs for scalability and performance

    • Distributed training and inference
  • 9

    Study Material

    • Study Material