Mastering OpenAI Python APIs: Unleash ChatGPT and GPT4
13h 4m 58s
English
Free
Course description
Unleash the Power of AI: Master OpenAI's APIs, including GPT-4, DALL-E, and Whisper in this Comprehensive and Hands-On Course. This is a brand new course, recorded with GPT-4! Step into the world of artificial intelligence and discover how to harness OpenAI's cutting-edge APIs, including GPT3, GPT-3.5, GPT4, DALL-E, and Whisper, to create groundbreaking applications and solutions. This immersive, project-driven course is designed for learners of all backgrounds and skill levels, providing a solid foundation in AI-driven development.
Read more about the course
In this comprehensive course, you will:
- Develop a deep understanding of OpenAI's generative models and their potential applications
- Master GPT-4 for natural language processing, including text generation, summarization, translation, and more
- Use GPT-4 to debug code, improve code, and even write code from scratch
- Generate visually stunning images and artwork using DALL-E based on textual prompts
- Convert spoken audio into accurate transcriptions and translations with the power of Whisper
- Use AI Embeddings to distill, index, search, and compare text, unveiling the geometric power for comparing words, paragraphs, and documents
Real-World Projects for Practical Experience:
- Create a dynamic Q&A Bot using GPT-4
- Create a visual color palette search engine with GPT-4
- Write an interactive code reviewing assistant with GPT-4
- Create an AI-powered Spotify playlist generator
- Analyze the sentiment of Reddit comments using GPT-4
- Summarize books of any size into a couple of paragraphs
- Create your own interactive, infinite Choose Your Own Adventure application with DALL-E and GPT-4
- Generate an Emedding-Powered movie recommendation algorithm
Throughout the course, you will engage in hands-on projects and real-world examples, allowing you to immediately apply your newly-acquired knowledge and skills. We also delve into:
- Best practices for prompt engineering, tokenization, and temperature settings
- Strategies for optimizing API performance, error handling, and resource management
- The ethical considerations and challenges associated with AI-driven development
Whether you're a software developer, data scientist, or an AI enthusiast, this course will equip you with the expertise to integrate OpenAI's APIs into your projects and create innovative AI-powered solutions.
Embark on your AI journey and transform your skills with this comprehensive and engaging course. Enroll today and start unlocking the limitless potential of OpenAI's GPT-4, DALL-E, and Whisper APIs!
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| # | Lesson Title | Duration |
|---|---|---|
| 1 | Welcome & Course Overview | 11:03 |
| 2 | A Brief History of OpenAI | 05:58 |
| 3 | Let's Talk About GPT | 06:33 |
| 4 | OPTIONAL: The Transformer Architecture | 11:25 |
| 5 | Signing Up For An Account | 03:48 |
| 6 | Getting The Course Code | 00:40 |
| 7 | Our First Completion Request | 08:07 |
| 8 | Hiding Our API Key | 06:08 |
| 9 | Understanding Tokens | 03:08 |
| 10 | max_tokens | 06:21 |
| 11 | Stop Sequences | 10:09 |
| 12 | N and Echo | 09:27 |
| 13 | The Different Completion Models | 10:10 |
| 14 | Comparing Model Performance and Pricing | 05:37 |
| 15 | The Elements of a Good Prompt | 10:36 |
| 16 | Controlling The Output Format | 07:34 |
| 17 | Summarization Prompts | 03:34 |
| 18 | Data Extraction Prompts | 03:06 |
| 19 | Sentiment Analysis Prompts | 03:27 |
| 20 | Zero-Shot Vs. Few-Shot Prompting | 04:47 |
| 21 | "Let's Think Step By Step" Prompting | 03:31 |
| 22 | Text Transformation Prompts | 05:04 |
| 23 | Introducing The Color Palette Project | 03:57 |
| 24 | Writing The Color Palette Generator Prompt | 08:44 |
| 25 | Writing a Color-Swatch Rendering Function | 11:31 |
| 26 | Setting Up The Flask Server | 06:03 |
| 27 | Integrating OpenAI With Server | 08:14 |
| 28 | Writing the Palette Endpoint | 08:43 |
| 29 | Creating The Form | 09:46 |
| 30 | Rending The Colors In The Browser | 06:24 |
| 31 | Copy and Paste Functionality | 06:21 |
| 32 | Styling The Color Blocks | 06:42 |
| 33 | Styling The Form | 07:14 |
| 34 | Refactoring Our Front-End Code | 07:04 |
| 35 | Temperature | 11:51 |
| 36 | Understanding Top P | 09:07 |
| 37 | Frequency Penalty | 10:03 |
| 38 | Presence Penalty | 04:54 |
| 39 | Streaming Responses | 06:35 |
| 40 | Introducing The Chat API | 03:37 |
| 41 | Our First Chat Request | 05:56 |
| 42 | Important Note On Pricing! | 02:13 |
| 43 | Prompting With Properly Formatted Messages | 08:18 |
| 44 | Note on GPT-3.5-Turbo Versions | 01:45 |
| 45 | Rewriting a Completion Prompt In Chat Format | 08:26 |
| 46 | Chat API Parameters | 05:07 |
| 47 | Introducing Our Chatbot Project | 02:29 |
| 48 | Writing The Basic Chatbot Structure | 07:53 |
| 49 | Persisting Messages Across Requests | 08:46 |
| 50 | Adding Optional Personalities | 09:21 |
| 51 | Colorizing The Chatbot Output | 05:29 |
| 52 | Asking GPT-4 To Explain Code | 06:08 |
| 53 | Calculating Time Complexity With GPT-4 | 07:10 |
| 54 | Translating JS To Python With GPT-4 | 06:12 |
| 55 | Fixing Code Bugs With GPT-4 | 06:30 |
| 56 | Generating Code From Scratch With GPT-4 | 06:17 |
| 57 | Counting Tokens With TikToken | 09:10 |
| 58 | Counting GPT-4 Message Tokens | 07:10 |
| 59 | Introducing The Basic Code Reviewer | 04:08 |
| 60 | Building The Basic Code Reviewer Pt 1 | 08:26 |
| 61 | Building The Basic Code Reviewer Pt 2 | 10:21 |
| 62 | Introducing The Interactive Code Reviewer | 07:45 |
| 63 | Interactive Code Reviewer Prompt | 13:00 |
| 64 | Interactive Code Reviewer Walkthrough | 12:04 |
| 65 | Introducing The Spotify Project | 04:40 |
| 66 | Writing The Playlist Generating Prompt | 12:11 |
| 67 | Finishing The Playlist Prompt | 05:38 |
| 68 | Getting Spotify Developer Credentials | 03:39 |
| 69 | Spotify Authentication Via Python | 07:31 |
| 70 | Searching For Spotify Tracks Via Python | 05:33 |
| 71 | Creating Spotify Playlists Programmatically | 04:52 |
| 72 | Adding in OpenAI | 06:46 |
| 73 | Accepting Command Line Arguments | 06:26 |
| 74 | Overview of the Polished Solution | 05:21 |
| 75 | Introducing Embeddings | 02:39 |
| 76 | Generating a Single Embedding | 03:50 |
| 77 | Introducing The Movie Embedding Visualization | 04:20 |
| 78 | Getting Our Movie Data Ready | 04:39 |
| 79 | Generating Embeddings for 5000 Movies | 12:08 |
| 80 | Visualizing Our Embeddings With Atlas | 09:34 |
| 81 | Recommending Movies Using Our Embeddings | 16:12 |
| 82 | Expanding GPT-4 "Knowledge" With Embeddings | 08:03 |
| 83 | Gathering Our Embeddings | 11:36 |
| 84 | Implementing Q&A With Embeddings & GPT-4 | 09:08 |
| 85 | Introducing The Reddit Sentiment Analysis Project | 04:59 |
| 86 | Collecting Comments From Reddit | 10:10 |
| 87 | Analyzing Sentiment In The Comments | 11:49 |
| 88 | Plotting The Results | 05:57 |
| 89 | Introducing The Book Summarizer | 07:24 |
| 90 | Preparing Book Text For Summarization | 07:16 |
| 91 | Context Window Math | 08:35 |
| 92 | Summarization Logic | 13:44 |
| 93 | Caching Results | 04:55 |
| 94 | Performing a "Meta-Summary" With GPT-4 | 07:56 |
| 95 | Intro To DALL-E | 03:46 |
| 96 | Making a DALL-E Request | 08:04 |
| 97 | Saving DALL-E Images | 12:50 |
| 98 | Alternative Approach To Saving Images | 07:38 |
| 99 | Requesting Image Variations | 08:24 |
| 100 | DALL-E Image Edits | 05:09 |
| 101 | Introducing Stability.AI and Stable Diffusion | 04:56 |
| 102 | Using the Stability SDK | 07:57 |
| 103 | Introducing The Choose Your Own Adventure Project | 05:34 |
| 104 | GPT-4 CYOA Text Generation | 09:58 |
| 105 | Stable Diffusion CYOA Image Generation | 09:06 |
| 106 | Introducing Whisper | 01:58 |
| 107 | The Basics of Whisper | 06:30 |
| 108 | Providing a Prompt to Whisper | 02:48 |
| 109 | Translating Audio With Whisper | 04:52 |
| 110 | Transcribing Non-English Audio | 05:01 |
| 111 | Running The Whisper Model Locally | 05:49 |
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