Langchain Guide: Next-Gen Chatgpt & Llms Apps With Langchain

From Zero to Hero. Build Real-World Next-Gen LLM App with LangChain, open-source LLMs, Hugging Face, FAISS and Pinecone

What you'll learn

Foundations of Language Models

Generative AI

Tool-box of Language Models (LLM) and NLP

Open-source Large Language Models (LLMs)

How to supercharge LLMs with LangChain

Training ChatGPT with a personalized knowledge base with LangChain

A deep dive into vector databases: FAISS, PINECONE, etc

Understanding multi-step reasoning

Langchain and Agents in enhancing LLM capabilities

Requirements

Python

Description

The Artificial Intelligence revolution is upon us, bringing a new wave of groundbreaking tools. One of these tools is LangChain, an innovative technology that helps AI professionals ramp up the capabilities of Language Models. In our LangChain course, we guide you to unleash the full potential of these tools, catapulting your AI skills to new heights.This course is not just about the basics of Generative Artificial Intelligence and Natural Language Processing. It's about using LangChain to supercharge the performance and efficiency of your Language Models. We'll arm you with the skills and insights to tweak and tailor language models to your specific requirements, opening up a wider array of AI challenges and opportunities for you to tackle.Imagine having the ability to train ChatGPT with your own custom knowledge base, and that's just the start. We'll delve into what vector databases are, get to grips with multi-step reasoning, and show you how LangChain can unlock new possibilities with your LLMs.In this course, we're going to cover:Getting to know Language ModelsThe nuts and bolts of Generative AIThe tool-box of Language Models (LLM) and NLPWorking with open-source Large Language Models (LLMs)How to supercharge LLMs with LangChainTraining ChatGPT with a personalized knowledge base with LangChainA deep dive into vector databasesUnderstanding multi-step reasoningThe role of Langchain and Agents in enhancing LLM capabilitiesSo, dive into the captivating world of Language Models with LangChain. Extend the capabilities of your LLM models, develop language models that cater to your needs, and explore a whole new world of possibilities with LLMs through LangChain.

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Introduction to Language Models

Lecture 2 Introduction to Language Models

Lecture 3 What are Language Models

Lecture 4 Types of Language Models

Section 3: Fundamentals of Generative AI

Lecture 5 Introduction to Generative AI and its applications ChatGPT, DALLE

Lecture 6 Discriminative vs. generative models

Lecture 7 GANs Generative Adversarial Networks

Lecture 8 Models based on Transformers

Lecture 9 Variational Auto Encoders and latent space

Lecture 10 Challenges of Generative AI

Section 4: Language Modeling Tools (LLM) and NLP

Lecture 11 Tools for working with LLMs and NLP

Lecture 12 Open AI API

Lecture 13 Hugging Face Fundamentals

Lecture 14 LangChain Fundamentals

Lecture 15 Open source LLM models

Section 5: Open-source Large Language Models (LLMs)

Lecture 16 Benefits of open-source LLM models

Lecture 17 Different open-source LLM models and comparative analysis

Lecture 18 Fundamentals of the Llama model

Lecture 19 Alpaca model fundamentals

Lecture 20 Fundamentals of the Vicuña model

Lecture 21 Koala model fundamentals

Section 6: Giving Superpowers to LLMs with LangChain

Lecture 22 Introduction to LangChain

Lecture 23 Different LangChain model types and requirements

Lecture 24 LLM input management with LangChain's Prompts Module

Lecture 25 Combination of LLM with other components through chains

Lecture 26 Providing access to external data through LangChain Indexes

Lecture 27 Giving the ability to memorize ChatGPT through Memor LangChain

Lecture 28 Providing access to tools through LangChain's Agents module

Section 7: Train ChatGPT with a customized knowledge base

Lecture 29 Introduction to LangChain indexes

Lecture 30 Practical Lab: ChatGPT training with complete inforPDF

Section 8: Vector Databases

Lecture 31 Introduction to vector databases and importance for LLMs

Lecture 32 Characteristics of vector databases

Lecture 33 Vector Databases, Plugins and Vector Libraries

Lecture 34 Vector search strategies and similarity metrics

Section 9: Multi-stage reasoning

Lecture 35 Programming WorkFlows in LangChain

Lecture 36 Linking multiple LLMs with LangChain

Lecture 37 Practical Lab: Chaining of Prompts with LangChain Chains

Lecture 38 Practical Lab (II): Chaining Prompts with LangCha Chains

Section 10: Langchain and Agents: Giving new capabilities to LLMs

Lecture 39 Introduction to LangChain agents

Lecture 40 Hands-on Lab: Programming the Wikipedia, Google, and Google agent

Lecture 41 Practical Lab: Integration of agents in ChatGPT

AI Enthusiasts keen to expand their understanding of Language Models and Generative AI.,Anyone interested in Large Language Models,Python Developers interested in integrating advanced AI techniques into their applications.,Data Scientists aiming to broaden their skill set in the field of Natural Language Processing.,Tech Professionals seeking to stay ahead in the rapidly evolving landscape of AI,Researchers in AI and Machine Learning looking for practical application of theoretical knowledge