Introduction

The primary goal of the CASPA AI Lab is to offer real-world, advanced GenAI projects to college students and graduate students. We expect students to be proactive, embody a can-do attitude, and possess the spirit of self-directed learning. The GenAI development stack is quite intricate as shown as follows. Our initial projects will be application-driven. In the future, we may delve into trending topics specific to certain domains and state-of-the-art concepts. These could include creating AI products for the semiconductor industry, making AI deployment more cost-effective, building AI-data centers, using AI agents to automate workflows and tasks, and more.

Let’s keep building!

Guide for AI Learning

Some websites like: https://www.datacamp.com/blog/how-to-learn-ai, actually provides a very comprehensive guide for AI beginners. Some of the things that we are not going to teach in our AI labs and we assume everyone should learn by yourself.

• Statistics: Statistics is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. It provides the backbone for understanding and working with data in AI.
• Mathematics: As discussed earlier, certain areas of mathematics form the foundation of AI algorithms. Linear algebra, calculus, probability, and differential equations are all mathematical tools that will be used in your AI journey.
• Programming: Knowing how to write code allows you to develop AI algorithms, manipulate data, and use AI tools and libraries. Python is currently the most popular language in the AI community due to its simplicity, flexibility, and availability of data science libraries.
• Data structures: Data structures allow you to store, retrieve, and efficiently manipulate data. Therefore, knowledge of data structures like arrays, trees, lists, and queues is essential for writing efficient code and developing complex AI algorithms.
• Data manipulation: Data manipulation involves cleaning, transforming, and manipulating data to prepare it for further analysis or feeding it into AI models. Skills in using libraries like pandas, numpy, for data manipulation are essential for working in AI.
• Tools and Framework: Knowing the right tools and packages is crucial to your success in AI. Pytorch, Tensorflow, Hugging Face Trasnformers, Langchain, OpenAI API, etc.

You can also learn from experts including OpenAI founding team, Andrej Karpathy

https://www.youtube.com/@AndrejKarpathy, https://github.com/karpathy

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