Merge pull request #434 from USDA-SCINet/add-events

Add AI practicum events

_includes/categories.html

@@ -24,6 +24,9 @@
   {% if page.filter.provider %}
     {% assign myposts = myposts | where_exp: "post", "post.provider == page.filter.provider" %}
   {% endif %}
+  {% if page.filter.other %}
+    {% assign myposts = myposts | where_exp: "post", "post.filtered == page.filter.other" %}
+  {% endif %}
   {% if page.filter.external %}
     {% assign all_external = "true" %}
   {% endif %}

pages/training_events/virtual/practicum-ai.md

@@ -0,0 +1,32 @@
+---
+title: Practicum AI
+description: Practicum AI is a hands-on applied artificial intelligence (AI) curriculum intended for learners with limited coding and math background.
+permalink: /training/practicum-ai
+layout: page
+
+filter:
+  other: practicumai
+
+collect: events
+collect_title: Training Calendar
+
+sidenav_link: /training/resources
+sidenav_append: 
+  - title: Practicum AI
+    url: /training/practicum-ai
+
+subnav:
+  - title: Training Calendar
+    url: '#training-calendar'
+---
+
+Developed and presented by the University of Florida and customized for USDA-ARS with funding from ARS’s AI Center of Excellence, *Practicum AI* is a hands-on applied artificial intelligence (AI) curriculum intended for learners with limited coding and math background. Using hands-on exercises and graphically based, conceptual content, the program starts from introductory content and builds your AI knowledge, enabling you to design and conduct AI work. **Starting this fall, the University of Florida Research Computing team will be offering a series of courses to help ARS researchers begin using AI.** In January, a more advanced course on computer vision will be offered.
+
+To register for these courses, please [fill out the registration form](https://forms.office.com/g/YnnYsxX9e3). You do not need to register for all courses and may instead register for only the courses that are most relevant to your work. You will need a SCINet account for all but Course 1. If you do not have a SCINet account, you may [request one](/about/signup).
+
+FY24 (Q1 and Q2) Practicum AI courses:
+Each course will be on Monday and Wednesday from 1:00-5:00 pm Eastern Time.
+1.	Oct 30 & Nov 1: *[Getting Started with AI](/events/2023-10-30-Practicum-AI#course-1--getting-started-with-ai)*
+1.	Nov 6 & Nov 8: *[Computing for AI](/events/2023-11-06-Practicum-AI#course-2--computing-for-ai)*
+1.	Dec 4 & Dec 6: *[Python for AI](/events/2023-12-04-Practicum-AI#course-3--python-for-ai)*
+1.	Jan 8 & Jan 10: *[Deep Learning Foundations](/events/2024-01-08-Practicum-AI#course-4--deep-learning-foundations)*

sn_collections/_events/2023-10-30-Practicum-AI.md

@@ -0,0 +1,52 @@
+---
+
+title: "Practicum AI - Getting Started with AI"
+excerpt: "This course provides a high-level overview of the concepts, history, and vocabulary of AI. You will get hands-on experience with simple online AI tools, experimenting with gathering data, and training a model." 
+type: training
+provider: University of Florida
+hideprovider: true
+tags: Artificial-Intelligence
+end_date: 2023-11-01
+filtered: practicumai
+registration:
+    text: Practicum AI Registration
+    url: https://forms.office.com/g/YnnYsxX9e3
+
+sessions: 
+  - session:
+    time: 1:00 PM - 5:00 PM ET
+    multiday: "Oct 30 & Nov 1"
+
+
+sidenav_append: 
+  - title: Practicum AI
+    url: /events/2023-10-30-Practicum-AI
+
+subnav:
+  - title: Getting Started with AI
+    url: '#course-1--getting-started-with-ai'
+  - title: Computing for AI
+    url: '/events/2023-11-06-Practicum-AI#course-2--computing-for-ai'
+  - title: Python for AI
+    url: '/events/2023-12-04-Practicum-AI#course-3--python-for-ai'
+  - title: Deep Learning Foundations
+    url: '/events/2024-01-08-Practicum-AI#course-4--deep-learning-foundations'
+
+---
+
+# Practicum AI
+
+[*Practicum AI*](/training/practicum-ai) is a hands-on applied artificial intelligence (AI) curriculum intended for learners with limited coding and math background. To register for these courses, please [fill out the registration form](https://forms.office.com/g/YnnYsxX9e3). You do not need to register for all courses and may instead register for only the courses that are most relevant to your work. 
+
+## Course 1 – Getting Started with AI
+
+This course provides a high-level overview of the concepts, history, and vocabulary of AI. You will get hands-on experience with simple online AI tools, experimenting with gathering data, and training a model. We will review the general process that underlies most AI application development pathways and briefly touch on ethical aspects of AI that will also help ensure more reliable AI models.  
+
+**Prerequisites –** None! This is our introductory course and assumes no technical background.  
+**Objectives** – Learners will be able to:
+1.	Understand the interplay among and definitions of AI, machine learning, and data science.
+1.	Recall significant historical events in AI and the advantages of high-performance computing for AI development.
+1.	Recognize four real-world AI use cases, including datasets and model types.
+1.	Comprehend the nature of AI models, identify major types, and the features required for each.
+1.	Understand the AI application development process.
+1.	Appreciate key principles in AI: transparency, inclusion, accountability, reliability, and security/privacy.

sn_collections/_events/2023-11-06-Practicum-AI.md

@@ -0,0 +1,59 @@
+---
+
+title: "Practicum AI - Computing for AI"
+excerpt: "In this course, you will learn about some of the tools recommended for building, testing, tweaking, and deploying AI models. You will learn about Jupyter Notebooks, Git, GitHub, and high-performance computing (HPC) environments." 
+type: training
+provider: University of Florida
+hideprovider: true
+tags: Artificial-Intelligence git
+end_date: 2023-11-08
+filtered: practicumai
+registration:
+    text: Practicum AI Registration
+    url: https://forms.office.com/g/YnnYsxX9e3
+
+sessions: 
+  - session:
+    time: 1:00 PM - 5:00 PM ET
+    multiday: "Nov 6 & Nov 8"
+    prerequisites:
+      - text: Have a SCINet account and be able to login 
+        url: /about/signup
+      - text: Have a Github account
+
+
+sidenav_append: 
+  - title: Practicum AI
+    url: /events/2023-11-06-Practicum-AI
+
+subnav:
+  - title: Getting Started with AI
+    url: '/events/2023-10-30-Practicum-AI#course-1--getting-started-with-ai'
+  - title: Computing for AI
+    url: '#course-2--computing-for-ai'
+  - title: Python for AI
+    url: '/events/2023-12-04-Practicum-AI#course-3--python-for-ai'
+  - title: Deep Learning Foundations
+    url: '/events/2024-01-08-Practicum-AI#course-4--deep-learning-foundations'
+---
+
+# Practicum AI
+
+[*Practicum AI*](/training/practicum-ai) is a hands-on applied artificial intelligence (AI) curriculum intended for learners with limited coding and math background. To register for these courses, please [fill out the registration form](https://forms.office.com/g/YnnYsxX9e3). You do not need to register for all courses and may instead register for only the courses that are most relevant to your work.  You will need a SCINet account for this course. If you do not have a SCINet account, you may [request one](/about/signup).
+
+
+## Course 2 – Computing for AI
+
+This course is the second in the Practicum AI series. This course can also be taken on its own to familiarize yourself with some of the important tools used in data science and scientific computing.
+
+In this course, you will learn about some of the tools recommended for building, testing, tweaking, and deploying AI models. You will learn about Jupyter Notebooks, Git, GitHub, and high-performance computing (HPC) environments. These are the key technologies that have become the industry standards for hands-on applied AI research and development.
+
+**Prerequisites** – 1) A GitHub account and 2) a SCINet account.  
+**Objectives** – Learners will be able to:
+1.	Describe essential technologies for hands-on AI applications, including Jupyter notebooks.
+2.	Understand the role of Jupyter notebooks in AI data analysis and model development.
+3.	Launch and use a Jupyter notebook session on SCINet’s Atlas cluster.
+4.	Recognize the significance of version control for open and reproducible AI projects.
+5.	Create and use a git repository.
+6.	Grasp the computational demands of AI and the need for GPU-enhanced high-performance computing environments.
+

sn_collections/_events/2023-12-04-Practicum-AI.md

@@ -0,0 +1,59 @@
+---
+
+title: "Practicum AI - Python for AI"
+excerpt: "Most hands-on artificial intelligence work is currently done using the Python programming language. As such, some understanding of Python and computer programming is needed to be successful in applying AI. That said, it is truly astounding how much complex AI research can be accomplished with a few lines of code!
+
+The content in this workshop is aimed at beginning coders who may have never programmed before. As with the rest of the Practicum AI workshops, we use Jupyter Notebooks for the learning experience." 
+type: training
+provider: University of Florida
+hideprovider: true
+tags: Artificial-Intelligence Python Jupyter
+end_date: 2023-12-06
+filtered: practicumai
+registration:
+    text: Practicum AI Registration
+    url: https://forms.office.com/g/YnnYsxX9e3
+
+sessions: 
+  - session:
+    time: 1:00 PM - 5:00 PM ET
+    multiday: "Dec 4 & Dec 6"
+    prerequisites:
+      - text: Have a SCINet account and be able to login 
+        url: /about/signup
+      - text: Understanding git and GitHub
+      - text: Navigating Jupyter notebooks on Atlas
+
+
+sidenav_append: 
+  - title: Practicum AI
+    url: /events/2023-12-04-Practicum-AI
+
+subnav:
+  - title: Getting Started with AI
+    url: '/events/2023-10-30-Practicum-AI#course-1--getting-started-with-ai'
+  - title: Computing for AI
+    url: '/events/2023-11-06-Practicum-AI#course-2--computing-for-ai'
+  - title: Python for AI
+    url: '#course-3--python-for-ai'
+  - title: Deep Learning Foundations
+    url: '/events/2024-01-08-Practicum-AI#course-4--deep-learning-foundations'
+---
+
+# Practicum AI
+
+[*Practicum AI*](/training/practicum-ai) is a hands-on applied artificial intelligence (AI) curriculum intended for learners with limited coding and math background. To register for these courses, please [fill out the registration form](https://forms.office.com/g/YnnYsxX9e3). You do not need to register for all courses and may instead register for only the courses that are most relevant to your work.  You will need a SCINet account for this course. If you do not have a SCINet account, you may [request one](/about/signup).
+
+## Course 3 – Python for AI 
+
+Most hands-on artificial intelligence work is currently done using the Python programming language. As such, some understanding of Python and computer programming is needed to be successful in applying AI. That said, it is truly astounding how much complex AI research can be accomplished with a few lines of code!
+
+The content in this workshop is aimed at beginning coders who may have never programmed before. As with the rest of the Practicum AI workshops, we use Jupyter Notebooks for the learning experience. A Jupyter Notebook is an easy-to-use, yet powerful tool that supports interactive coding as well as nicely formatted explanatory text. Much of exploratory AI research is conducted in Jupyter Notebooks, and it is easy to transfer code from Notebooks to scripts when it is time to scale up analyses.
+
+**Prerequisites** – Computing for AI content: Understanding git and GitHub, navigating Jupyter notebooks on Atlas.  
+**Objectives** – Learners will be able to:
+1.	Understand Python's dominance in data science and its role in high-performance computing.
+2.	Develop Python troubleshooting skills, including documentation referencing and error handling.
+3.	Write Python code adhering to best practices for operators, variables, and functions.
+4.	Utilize Python libraries through the import function and understand major AI libraries like TensorFlow and Keras.
+5.	Manipulate and visualize data using Pandas and Matplotlib.

sn_collections/_events/2024-01-08-Practicum-AI.md

@@ -0,0 +1,59 @@
+---
+
+title: "Practicum AI - Deep Learning Foundations"
+excerpt: "Deep learning is the focus of modern AI. Models have many layers and millions, or now approaching a trillion, parameters! This course breaks things down and introduces you to a small AI model to provide a conceptual understanding of how AI models are built, trained, and deployed." 
+type: training
+provider: University of Florida
+hideprovider: true
+tags: Artificial-Intelligence Python Jupyter
+end_date: 2024-01-10
+filtered: practicumai
+registration:
+    text: Practicum AI Registration
+    url: https://forms.office.com/g/YnnYsxX9e3
+
+sessions: 
+  - session:
+    time: 1:00 PM - 5:00 PM ET
+    multiday: "Jan 8 & Jan 10"
+    prerequisites:
+      - text: Have a SCINet account and be able to login 
+        url: /about/signup
+      - text: Understanding git and GitHub
+      - text: Navigating Jupyter notebooks on Atlas
+      - text: Basics of Python coding and Pandas
+
+
+sidenav_append: 
+  - title: Practicum AI
+    url: /events/2024-01-08-Practicum-AI
+
+subnav:
+  - title: Getting Started with AI
+    url: '/events/2023-10-30-Practicum-AI#course-1--getting-started-with-ai'
+  - title: Computing for AI
+    url: '/events/2023-11-06-Practicum-AI#course-2--computing-for-ai'
+  - title: Python for AI
+    url: '/events/2023-12-04-Practicum-AI#course-3--python-for-ai'
+  - title: Deep Learning Foundations
+    url: '#course-4--deep-learning-foundations'
+---
+
+# Practicum AI
+
+[*Practicum AI*](/training/practicum-ai) is a hands-on applied artificial intelligence (AI) curriculum intended for learners with limited coding and math background. To register for these courses, please [fill out the registration form](https://forms.office.com/g/YnnYsxX9e3). You do not need to register for all courses and may instead register for only the courses that are most relevant to your work.  You will need a SCINet account for this course. If you do not have a SCINet account, you may [request one](/about/signup).
+
+## Course 4 – Deep Learning Foundations
+
+Deep learning is the focus of modern AI. Models have many layers and millions, or now approaching a trillion, parameters! This course breaks things down and introduces you to a small AI model to provide a conceptual understanding of how AI models are built, trained, and deployed.
+
+**Prerequisites** – Python for AI content: Basics of Python coding and Pandas  
+**Objectives** – Learners will be able to:
+1.	Define neural networks, their basic components (neurons, perceptrons, bias, weights), and activation functions.
+2.	Understand the significance of deep networks in neural computation.
+3.	Utilize deep learning for image tasks, from classification to training with Keras' MNIST package.
+4.	Dive into TensorFlow and perceptron implementation.
+5.	Grasp key concepts like gradient descent, optimizers, and the chain rule.
+6.	Discuss the role of loss functions in network training.
+7.	Explore applications in image classification and Natural Language Processing using pre-trained models.
+