Building Generative Ai Projects With Llm, Langchain, Gan

Learn how to build generative AI apps using large language models, langchain, and generative adversarial networks

What you'll learn

Learn the basic fundamentals of large language model and generative adversarial network, such as getting to know their use cases and understanding how they work

Learn how to build legal document analyzer using LLM

Learn how to analyze Excel data using LLM

Learn how to build AI short story generator using LLM

Learn how to build AI code generator using LLM

Learn how to build customer support chatbot using LLM

Learn how to build report summarizer using LLM

Learn how to build AI travel planner using Langchain

Learn how to build AI math solver using Langchain

Learn how to build AI random face generator using ProGAN

Learn how to build random digital art generator using Deep Convolutional GAN

Learn how to build generator and discriminator functions

Learn how to train and fine tune GAN model

Learn how to create user interface using Streamlit and deploy app to Hugging Face Space

Learn how to build LLM based apps using Dify AI and Relevance AI

Learn how to find AI models in Hugging Face and download dataset from Kaggle

Requirements

No previous experience in LLM is required

Basic knowledge in Python

Description

Welcome to Building AI Projects with LLM, Langchain, GAN course. This is a comprehensive project based course where you will learn how to develop advanced AI applications using Large Language Models, integrate workflow using Langchain, and generate images using Generative Adversarial Networks. This course is a perfect combination between Python and artificial intelligence, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in generative AI integration. In the introduction session, you will learn the basic fundamentals of large language models and generative adversarial networks, such as getting to know their use cases and understand how they work. Then, in the next section, you will find and download datasets from Kaggle, it is a platform that offers a diverse collection of datasets. Afterward, you will also explore Hugging Face, it is a place where you can access a wide range of ready to use pre-trained models for various AI applications. Once everything is ready, we will start building the AI projects. In the first section, we are going to build a legal document analyzer, where users can upload a PDF file, and AI will extract key information, summarize complex legal texts, and highlight important clauses for quick review. Next, we will develop an Excel data analyzer, enabling users to upload spreadsheets and leverage AI to identify trends, generate insights, and automate data analysis processes. Then after that, we will create an AI short story generator, where users can generate creative and engaging narratives based on simple prompts, making it a useful tool for writers and content creators. Following that, we will build an AI code generator, where users can input natural language descriptions, and AI will generate structured, functional code snippets, streamlining the coding process. In the next section, we will develop a Q&A customer support chatbot, capable of answering common inquiries based on a given knowledge base, providing automated customer service responses. In addition, we will also create an AI-powered summarizer, designed to condense lengthy articles, research papers, or reports into concise summaries, helping users quickly understand key points. Moving on to LangChain, we will build a travel planner that takes user preferences and generates personalized itineraries, making trip planning easier and more efficient. Then, we will also create a math problem solver that interprets and solves mathematical equations step by step, helping students and professionals understand problem-solving techniques. In the following section, we will create GAN projects, for the first project, we will develop a random face generator, which can create realistic human faces from scratch, demonstrating the power of generative AI in producing lifelike imagery. In the second project, we will build a deep convolutional GAN from scratch by implementing the generator and discriminator functions, defining a loss function, and training the model using an adversarial learning approach to generate realistic images. Once we have built the apps we will conduct testing to make sure the app has been fully functioning and we will also deploy the app. Lastly, at the end of the course, we will build an LLM based app using no code tools like Dify AI and Relevance AI. By using these tools, you will be able to speed up the development process.First of all, before getting into the course, we need to ask ourselves this question, why should we build apps using a large language model? Well, here is my answer, LLMs can be used for analyzing context, automating complex text-based tasks, and generating human-like responses. These technologies not only streamline workflows and accelerate information retrieval but also improve accuracy in text generation and data processing.Whether it's content creation, document analysis, or chat-based interactions, LLMs make AI driven solutions more efficient and accessible.Below are things that you can expect to learn from this course:Learn the basic fundamentals of large language model and generative adversarial network, such as getting to know their use cases and understanding how they workLearn how to find AI models in Hugging Face and download dataset from KaggleLearn how to build legal document analyzer using LLMLearn how to analyze Excel data using LLMLearn how to build AI short story generator using LLMLearn how to build AI code generator using LLMLearn how to build customer support chatbot using LLMLearn how to build report summarizer using LLMLearn how to build AI travel planner using LangchainLearn how to build AI math solver using LangchainLearn how to build AI random face generator using ProGANLearn how to build random digital art generator using Deep Convolutional GANLearn how to build generator and discriminator functionsLearn how to train and fine tune GAN modelLearn how to create user interface using Streamlit and deploy app to Hugging Face SpaceLearn how to build LLM based apps using Dify AI and Relevance AI

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Introduction to LLM & GAN

Lecture 5 Introduction to LLM & GAN

Section 4: Finding & Downloading Datasets From Kaggle

Lecture 6 Finding & Downloading Datasets From Kaggle

Section 5: Finding AI Models in Hugging Face

Lecture 7 Finding AI Models in Hugging Face

Section 6: Building Legal Document Analyzer with LLM

Lecture 8 Building Legal Document Analyzer with LLM

Section 7: Analyzing Excel Data with LLM

Lecture 9 Analyzing Excel Data with LLM

Section 8: Building AI Short Story Generator with LLM

Lecture 10 Building AI Short Story Generator with LLM

Section 9: Building AI Code Generator with LLM

Lecture 11 Building AI Code Generator with LLM

Section 10: Building Customer Support Chatbot with LLM

Lecture 12 Building Customer Support Chatbot with LLM

Section 11: Building Report Summarizer with LLM

Lecture 13 Building Report Summarizer with LLM

Section 12: Building AI Travel Planner with Langchain

Lecture 14 Building AI Travel Planner with Langchain

Section 13: Building AI Math Solver with Langchain

Lecture 15 Building AI Math Solver with Langchain

Section 14: Building AI Random Face Generator with ProGAN

Lecture 16 Building AI Random Face Generator with ProGAN

Section 15: Building Deep Convolutional GAN Model From Scratch

Lecture 17 Building Generator & Discriminator Functions

Lecture 18 Training Deep Convolutional GAN Model

Lecture 19 Generating Digital Art with Deep Convolutional GAN Model

Section 16: Creating User Interface with Streamlit & Deploying App on Hugging Face Space

Lecture 20 Creating User Interface with Streamlit & Deploying App on Hugging Face Space

Section 17: Building LLM Based Apps with Dify AI & Relevance AI

Lecture 21 Building LLM Based Apps with Dify AI & Relevance AI

Section 18: Conclusion & Summary

Lecture 22 Conclusion & Summary

AI Engineers who are interested in building generative AI apps using LLMs and Langchain,Data scientists who are interested in performing data augmentation using GANs