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!
Watch Online
0:00 0:00
| # | 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 |
Comments
0 commentsWant to join the conversation?
Sign in to commentSimilar courses
Mathematical Foundations of Machine Learning
Sources: udemy
Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the mo
16 hours 25 minutes 26 seconds
Python for Data Science and Machine Learning Bootcamp
Sources: udemy
Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analy
24 hours 49 minutes 42 seconds
Complete linear algebra: theory and implementation
Sources: udemy
You need to learn linear algebra! Linear algebra is perhaps the most important branch of mathematics for computational sciences, including machine learning, AI, data science, st...
32 hours 53 minutes 26 seconds
Build AI startups with ChatGPT and AI Art
Sources: Code4Startup (coderealprojects)
In this series, you will build 9 AI-powered apps. Learn to leverage cutting-edge AI technologies to create innovative and impactful startups. The course...
6 hours 9 minutes 3 seconds
Developing LLM App Frontends with Streamlit
Sources: zerotomastery.io
This byte-sized course will teach Streamlit fundamentals and how to use Streamlit to create a frontend for your LLM-powered applications.
1 hour 43 minutes 52 seconds