There are several resources and exercises available to help you refresh your practical AI development skills, particularly focusing on setting up GPUs, testing, training, and debugging. Here are some recommendations:

1. Google's Machine Learning Crash Course: This course offers a variety of programming exercises that run directly in your browser using the Colaboratory platform. It covers topics such as linear regression, logistic regression, classification, neural networks, and more. The exercises are designed to help you understand and apply machine learning concepts effectivelydevelopers.google.com+1.

2. Run.ai Guides: These guides provide insights into selecting the best GPUs for deep learning, considering factors like memory, performance, and scalability. They also discuss the importance of GPUs in accelerating training times and offer recommendations for both consumer and data center GPUsrun.ai+1.

3. Coursera Courses: Coursera offers a range of GPU-related courses that can help you build job-ready skills in AI and machine learning. These courses cover topics such as computer vision, deep learning, and neural networks, and provide hands-on experience with tools like TensorFlow and PyTorchcoursera.org.

4. Cornell Virtual Workshop: This workshop offers exercises focused on understanding GPU architecture. The exercises involve compiling and running programs on the host that execute predefined CUDA methods on attached devices, helping you become familiar with important GPU featurescvw.cac.cornell.edu.

5. AI-Powered Tools for Debugging and Testing: Articles and guides discuss the use of AI tools to automate software debugging and testing, which can save developers time and effort. These tools can help identify and fix code errors, generate code snippets, and improve software qualitymedium.com+1.

6. DeepLearning.AI: This platform offers courses like "AI Python for Beginners," which teach Python programming with a focus on AI assistance. You'll learn how to write, test, and debug code efficiently, and create real-world AI applicationsdeeplearning.ai.

These resources should provide a comprehensive refresher on the practical aspects of AI development, including GPU setup, testing, training, and debugging.

Certainly! Here's a structured curriculum focused on practical AI development, including setting up GPUs, testing, training, and debugging. This curriculum is designed to build your skills progressively over several weeks.

Week 1-2: Foundations of Machine Learning

• Objective: Understand the basics of machine learning and essential algorithms.
• Topics:
  • Introduction to Machine Learning
  • Linear Regression and Logistic Regression
  • Basic Classification Techniques
• Resources:
  • Google's Machine Learning Crash Course: Linear Regression, Logistic Regression modulesdevelopers.google.com+1.
  • Hands-on exercises using Python and Jupyter Notebooks.

Week 3-4: Working with Data

• Objective: Learn how to preprocess and handle different types of data for machine learning.
• Topics:
  • Data Cleaning and Normalization
  • Handling Categorical and Numerical Data
  • Feature Engineering
• Resources:
  • Google's Machine Learning Crash Course: Working with Numerical Data, Working with Categorical Data modulesdevelopers.google.com.
  • Practical exercises on data manipulation using Pandas and NumPy.

Week 5-6: Neural Networks and Deep Learning

• Objective: Understand the fundamentals of neural networks and deep learning.
• Topics:
  • Introduction to Neural Networks
  • Activation Functions and Hidden Layers
  • Training Neural Networks
• Resources:
  • Google's Machine Learning Crash Course: Neural Networks moduledevelopers.google.com.
  • Implement basic neural networks using TensorFlow or PyTorch.

Week 7-8: GPU Setup and Optimization

• Objective: Learn how to set up and optimize GPUs for deep learning tasks.
• Topics:
  • Introduction to GPUs and CUDA
  • Setting Up GPUs for Deep Learning
  • Optimizing GPU Performance
• Resources:
  • Run.ai Guides on selecting and optimizing GPUs for deep learningrun.ai+1.
  • Hands-on setup of a GPU environment using NVIDIA tools.

Week 9-10: Advanced Deep Learning Techniques

• Objective: Explore advanced topics in deep learning and their applications.
• Topics:
  • Embeddings and Large Language Models
  • Transfer Learning and Fine-Tuning
  • Model Deployment and Scaling
• Resources:
  • Google's Machine Learning Crash Course: Embeddings, Large Language Models modulesdevelopers.google.com.
  • Project: Develop and deploy a simple deep learning model.

Week 11-12: Testing, Debugging, and Best Practices

• Objective: Learn best practices for testing and debugging machine learning models.
• Topics:
  • Unit Testing and Integration Testing
  • Debugging Techniques for Machine Learning
  • Model Evaluation and Validation
• Resources:
  • AI-Powered Tools for Debugging and Testing guidesmedium.com+1.
  • Implement testing and debugging practices in your projects.

Week 13-14: Capstone Project

• Objective: Apply your knowledge to a comprehensive project.
• Topics:
  • Project Planning and Execution
  • Documentation and Presentation
• Resources:
  • Choose a project idea and develop it using the skills acquired.
  • Present your project findings and insights.

Assessment and Review

• Objective: Evaluate your learning and identify areas for improvement.
• Activities:
  • Complete quizzes and assessments from the courses.
  • Reflect on your learning journey and plan next steps.

This curriculum provides a structured approach to learning practical AI development skills. You can adjust the pace and topics based on your prior knowledge and learning goals.