What you'll learn

Deep Learning

Transformers

Langchain

Large Language Models

Requirements

Python Knowledge

Basic Deep Learning Knowledge

Transformers knowledge is desirable

Description

Dive into the revolutionary world of Large Language Models (LLMs) with our comprehensive 4-hour workshop, designed to bridge the gap between theoretical knowledge and practical skills. Whether you're a budding data scientist, an AI enthusiast, or a seasoned professional looking to expand your toolkit, this course is tailored to empower you with hands-on experience in leveraging LLMs for a variety of real-world applications.What You'll Learn:Fundamentals and Advanced Techniques: Start with the basics of Large Language Models, including their architecture and capabilities, before progressing to advanced optimization methods such as Quantization and LoRA.Practical Exercises: Engage in structured exercises using Kaggle datasets in Colab, fine-tuning models for tasks like question answering and text summarization with QLoRA, and exploring cutting-edge concepts such as Retrieval Augmented Generation (RAG).Real-World Applications: Tackle engaging projects like building a semantic search engine to find movies and developing a chat interface with scholarly articles, applying your knowledge in tangible, impactful ways.Model Publication: As a bonus, learn how to share your fine-tuned models with the world through Huggingface, enhancing your visibility in the AI community.Intended Learners:This course is perfect for individuals looking to deepen their understanding of LLMs and apply these models in innovative ways. Ideal for AI professionals, data scientists, and researchers eager to expand their skills and apply LLMs to solve complex problems.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 How to use any dataset on Kaggle in Colab

Lecture 3 LLMs Training Optimization Methods - Quantization & LoRA

Lecture 4 What is RAG ?

Lecture 5 Evaluation Methods for LLMs

Section 2: Full Fine-tuning for Question Answering

Lecture 6 Introduction to the problem & the dataset

Lecture 7 Data preparation

Lecture 8 Model and tokenizer

Lecture 9 Training

Lecture 10 Evaluation and Testing

Section 3: Fine-tuning for News-Text Summarization (QLoRA)

Lecture 11 Introduction to the problem & the dataset

Lecture 12 Data preparation

Lecture 13 Tokenizer

Lecture 14 Model Preparation

Lecture 15 Training

Lecture 16 Evaluation

Lecture 17 Extra: Publish your model on Huggingface

Section 4: Find your movie - Semantic Search

Lecture 18 Introduction to the problem & dataset

Lecture 19 Data Preparation

Lecture 20 Vector Database creation

Lecture 21 Testing

Section 5: Chat with your paper - Retrieval Augmented Generation (RAG)

Lecture 22 Introduction to the problem & dataset

Lecture 23 Data Preparation & Preprocessing

Lecture 24 Vector Database creation

Lecture 25 LLM Prompt Integration & Model

Lecture 26 Testing

Deep learning Engineers,Deep Learning enthusiasts,Machine Learning Engineers,Artificial intelligence Engineers

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