OpenAI, GPT, ChatGPT and DALL-E Masterclass

Requirements

• Beginner and Intermediate level: None OpenAI, GPT, ChatGPT and DALL-E Masterclass Udemy

• Advanced level content: Programming skills in any language

Description

Welcome to the Masterclass for GPT, DALL-E and ChatGPT.

Ever since OpenAI arrived on the scene, access to a trained AI has become accessible to everyone.

• GPT allows you to ask a chatbot to complete tasks, and to answer questions

• Fine-tuning allows you to change the way the AI responds

• Embedding allows you to use your own knowledge base

• Dall-E allows you to generate images from text

In this course, you will learn very practical skills for using GPT. The skills can be used in the OpenAI playground or in programming code.

The course is split into 6 major parts:

1. Introduction and Prompt Engineering

2. Writing code and calling the API

3. Dall-E and Codex

4. Fine-tuning

5. Embedding

6. Writing and managing Chatbots

PART 1: Introduction and Prompt Engineering

In this section, you will learn how to get started with GPT. We also explain, design and use different types of prompts. At the end of the prompt engineering section, we give you two recipes you can use to get consistent results.

We'll see how GPT works, including:

• what tokens are

• high level overviews of GPT, Codex and Dall-E

• live examples of using the playground

• ways to improve prompts to get better results

• how to use templating to create reliable prompts

• how to use context to introduce new knowledge

This is already very practical.

We cover everything from "zero-shot" queries, through to multi-shot and advanced template prompts.

An entire section of the course is dedicated to creative writing for blogs, books and articles.

PART 2: Writing Code to use the API

In this section, you will learn how to write code to call GPT and the OpenAI API. Most of the examples are written in Python, but they are equally usable in C#, Typescript, Javascript, Node.js, PHP, Powershell and many other languages.

There are examples and explanations of how to adapt the code for each of these languages. We introduce various coding libraries and point out issues that may be specific to each language.

Once you start writing code, we go into all of the possible parameters and how they can change the way GPT works. We explain what they do, and ideal values for different situations.

As well as covering the completions endpoint, we also talk about the edit or instruction end point.

PART 3: CodeX and Dall-E

In this section, you will learn how you can use CodeX to generate, debug, and document code. We also give an example of how you can use CodeX to create unit tests for your functions.

You will find out how you can tell CodeX about new API calls and functions that are not part of its standard learning. We also use the edit endpoint to create an entire function from scratch.

Because CodeX generates code, we discuss the safety of using the code it generates and highlight several serious issues that you need to consider if you intend to use the code in production. We point out possible vulnerabilities and ways hackers can exploit the code it generates.

When it comes to Dall-E we walk through practical code examples to creating or edit images. We provide examples in multiple programming languages and explain how to handle images in memory and on your file system

PART 4: Fine Tuning

In this section we explain how you can fine-tune GPT. We explain the benefits and the difficulties. There are plenty of training examples and strategies you can use. We walk you through creating a set from scratch and uploading it OpenAI using your own code.

This followed by  an entire section explaining how you can use GPT to create its own trainings sets and how to get GPT to check and improve its own outputs.

To round out this section of the course, we go though all of the fine-tuning parameters. We explain how you can change settings to adjust the impact the rules have on the base training.

Part 5: Training on Large Text Documents using Embedding

Everyone wants to know how you can train GPT on large text documents. This section of the course explains how to take a text document and use it to answer questions using GPT. Embedding vectors are explained in great detail. We also explain the theory behind them so you know what GPT is doing. We talk about breaking up large text documents, creating embeddings, and searching the results.

We also talk about creating highly accurate classifiers and using clustering to find hidden patterns within documents and text.

Part 6: Chatbots

Who doesn't want their own chatbot? We explain how you can use GPT as a fully functional chatbot. We explain about personas and give you a recipe you can use to give your chatbot a personality and keep it on track.

To keep your chatbot on task and to give it persistent memory, we explain how you can use embedding to enhance the functionality and provide unique knowledge that is not part of the base GPT training.

UNIQUE FEATURES

• Every line of code explained in detail - email me any time if you disagree

• No wasted time "typing" on the keyboard like other courses. Instead we show finished code examples and prompts with detailed explanations that you can apply to your own use cases.

Thank you for reading and I hope to see you soon!

Who this course is for:

• Anyone who wants to master GPT and OpenAI

• Anyone who loves deep Natural Language Processing

• Anyone who wants to create their own chatbots or products using OpenAI and GPT