session 7 (#38)

* material for session 7 (#33)

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previous commits already here but not sync'd in fork

* update and add session 04 and topics #16
* add big data session.
* updates/fixes to big data session
* put javascript lib in site instead of downloading
* add a title to exercise
* remove unwanted extra html version of a markdown file
* add sessions 6 (A and B)
* fix links to slide presentations

docs/sessions/07_ai_apis.md

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+---
+title: 7 - AI APIs
+---
+
+# Azure AI Services and APIs
+
+## What is AI? 
+
+A discussion of AI and access to AI services in the cloud
+
+ - What is AI? Definitions and impressions from the fellowship
+
+    - What is Machine Learning? 
+
+> For a classification task, say 2 class, training mathematical model input is the 
+> features, output is the class.  Once the model knows what kind of features will predict 
+> the classes (based on hand-labeled).  Train the model to predict the category of new data.   
+> Unsupervised learning does not require pre-labelling, using clustering or other techniques 
+> to identify the features for classification
+
+ - How is AI different (if at all) from Machine Learning? 
+
+> AI is deep learning with larger data sets, faster and more reliably.  
+   
+ - What are the essential ingredients to create an AI system?
+ - What are the bias/issues with AI
+
+Would AI as we see it today be possible without cloud computing?
+
+ - Public AI vs Private AI: Google and Facebook have been working on this for a long time. 
+ - 'Cloud' versus 'Internal'
+
+## What is AI or ML? Services via functions
+
+- AI like Machine Learning in the end is a function with inputs and outputs
+    - Simple example: linear model from least squares ( aka regression)
+
+## Azure AI use cases
+
+*Class Discussion*
+
+## Azure Cognitive Services
+
+What are these services?   Massive machine learning models developed and trained by MS for public use
+
+ - "AI Services" : https://learn.microsoft.com/en-us/azure/ai-services/
+ - Vision: Image detection
+    - Object Detection https://learn.microsoft.com/en-us/azure/ai-services/computer-vision/concept-object-detection-40
+    - Use case: Deer census using camera traps : https://github.com/billspat/hpcc_megadetector
+
+Standard Practice for engagine with Azure Cognitive/AI services: 
+
+ 1. create 'account' in your resource group
+ 2. open 'studio' 
+ 3. interact visually
+ 4. learn how to use API (python)
+ 5. create code and use personal keys
+
+## OpenAI via Azure
+
+- What is ChatGPT and how can it be used? 
+- API Services
+   - Exploring using the "OpenAI Studio"
+   - Azure Service https://learn.microsoft.com/en-us/azure/ai-services/openai/
+   - OpenAI Service: https://openai.com/product
+   - Azure vs OpenAI
+
+
+## Example of using OpenAI API 
+
+Python Program pulled from several Azure Examples: 
+
+Azure OpenAI Demo Script: https://github.com/billspat/azure_openai_demo
+
+
+## Customizing your own AI
+
+- What is Transfer Learning?
+- Custom training from Azure , differentiating your AI.  
+
+## Acceptable Use
+
+We have an opportunity to use these tools as part of the fellowship via the MSU Azure acccount.   Please use these tools responsibly.  Please see the following policies
+
+ - MSU Acceptable Use Policy: https://tech.msu.edu/about/guidelines-policies/aup/
+ - Azure Responsible use of AI: https://learn.microsoft.com/en-us/azure/ai-services/responsible-use-of-ai-overview
+
+