—————- General Concepts —————-
0:00 | Introduction
0:40 | GPT-3/Codex/DALL·E
1:30 | Roadmap of video
2:35 | Creating the Azure OpenAI resource in your Azure portal
3:55 | Azure OpenAI Studio
3:56 | Features overview & application examples: summarization, classifying text, natural language to SQL, Generating new product names
7:58 | GPT-3 Playground overview
8:45 | Model deployments & how to create
9:05 | Models & considerations
9:23 | Model naming convention
9:59 | Overview of GPT-3 models and capabilities: Davinci, Babbage, Ada, Curie
11:35| Overview of Codex models and capabilities: Cushman & Davinci
12:05| Recommendation for initial model deployment
——————- Customizing Models ——————-
13:05| Generating python snippet from the GPT-3 playground
14:00| Defining the parameters to tune: Temperature, Max length tokens, top probabilities, frequency penalty, presence penalty, best of, pre-response text, post-response text
19:25| Scenarios on how to adjust model parameters
19:31| Scenario 1: low temperature, high top probability
21:06| Scenario 2: high temperature, high top probability, low frequency penalty
22:35| Scenario 3: moderate temperature, moderate top probability, low presence penalty
—————- Fine-tuning using the OpenAI API in Python —————-
24:15| Fine-tuning and use case, why would you want to fine-tune the model
24:42| Considerations: model size and its impact on computation and cost
25:20| Prompts and completions
25:50| Generating prompts and completions using the MediaWiki API to generate random titles and responses for those titles in Wikipedia
26:33| Importing necessary libraries
27:08| creating 100 random responses and writing a function to get their summaries, training data format
32:35| Training and validating datasets in JSON lines & exporting datasets to file management in Azure OpenAI
34:00| Creating customized model to fine-tine existing base models
35:30| Summary
———- Documentation ————
