The Complete Openai And Gpt Course - Build A Q&A Chatbot

[BlackHatX]

What you'll learn
The foundations of GPT and generative text - Large Language Models (LLM), Prompt Engineering
Receiver Augmented Generation (RAG) for Question Answering - its use cases and challenges, and real world implementation
Finetuning GPT models and their best practices, when and when not to fine tune.
Best practice strategies for troubleshooting issues with OpenAI APIs
Semantic Search - theory and Implementation
Vector databases, Pinecone - how they work, code samples
How to choose the right GPT model for completion and classification tasks
Understand how to use OpenAI's APIs and their production best practices
Tackling the LLM hallucination problem - what the problem is, and specific strategies to mitigate it.


Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Course Logistics and Important Announcements
Lecture 3 What We Are Building & Problem Statement
Lecture 4 FAQ
Lecture 5 Important Disclaimers
Section 2: Project 0: Create a ChatGPT Clone with Python and Streamlit
Lecture 6 Course Setup
Lecture 7 Course Project Solutions
Lecture 8 Building a ChatGPT Clone in 50 lines of Code
Lecture 9 Integrating OpenAI
Section 3: GPT3, Prompt Engineering, and LLMs
Lecture 10 ChatGPT, GPT3, InstructGPT - How They Work
Lecture 11 Prompt Engineering and Advanced GPT Parameters
Lecture 12 Why GPT Disrupted AI Industry
Section 4: Project 1: Intent Classifier
Lecture 13 IntentClassifier - What It is, Why It's Important
Lecture 14 Prompts for Classification Problems (Notebook)
Lecture 15 Evaluation GPT3.5 for Classification (Notebook)
Lecture 16 Integrate Intent Classifier into the App
Section 5: Limits of GPT - What It Can't Do
Lecture 17 Limitations of GPT - Knowledge Cutoff, Data Gaps, Token Limits
Lecture 18 Limits of GPT - Reasoning, Chain of Thought Prompting
Section 6: Project 2: Semantic Search and Retrievers
Lecture 19 Semantic Search Based Retrieval
Lecture 20 Word and Sentence Embeddings (Notebook)
Lecture 21 Semantic Search (Notebook)
Lecture 22 Vector Databases, Pinecone, Nearest Neighbor Search
Lecture 23 Integrating News Article Retriever into App
Section 7: Project 3: Retriever Augmented Question Answering and Fine Tuninng
Lecture 24 Question Answering with GPT, and Finetuning GPT Models
Lecture 25 Question Answering, Strategies for Handling Hallucinations (Notebook)
Lecture 26 Question Answering and Finetuning GPT (Notebook)
Lecture 27 Generative Labeling, Finetuning GPT, Model Evaluation (Notebook)
Lecture 28 App Integration
Section 8: Project 4: Summarization, External System Integration
Lecture 29 Document Summarization with GPT
Lecture 30 Summarization with GPT (Notebook)
Lecture 31 Adding Real Time Financial Charts (Notebook)
Lecture 32 App Integration
Lecture 33 Deployment
Python developers with some Pandas experience who are eager to build their first AI app using GPT library

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