Instructor Raghav Ganesh Sees Himself in the Students He Teaches Machine Learning to, Creative and Eager to Learn More
Raghav Ganesh is a freshman at Stanford University studying Computer Science. He has worked on research projects throughout high school, primarily focusing on ML in relation to images and healthcare datasets.
What has been the highlight of your teaching experience with Inspirit AI?
“The highlight of my experience teaching ML to students at Inspirit AI has been working with students during Office Hours and developing curriculum. It’s been great to get to meet students from all across the world and help them understand different concepts in ML. Whether it be going over the notebook from that day’s lesson, or diving deeper into the architecture for specific ML models, each day presents something new. Students also ask about other attributes of the AI learning process, ranging from tips for independent projects, to help with moving to the next stage after the Inspirit AI course. Working through blocks of code also brings forth great experiences. Whenever we work through more complicated problems or have to define an especially thorough function, I’m always intrigued to see how students have their own approaches to writing the solutions. It’s fun to work with beginner students as well, as it reminds me of when I got interested in ML and began working on my own projects. Watching middle school students and high school freshmen navigate through the notebooks has repeatedly impressed me.”
What advice would you give to young people who are interested in studying AI or pursuing a career in AI in the future?
“For any students interested in studying AI further, I would recommend that they start working on independent projects. Nowadays, you can find complex and interesting datasets online relatively quickly. By analyzing those and using them to develop your own custom ML models, you can get invaluable hands-on experience. You can also check out other aspects of CS, in tandem with ML exploration. Do you want to make web apps that can execute ML models in the browser? Web development tools like HTML, CSS, and Javascript can help you make those. Do you want to run ML models in web servers deployed in the cloud? Check out frameworks like Node.js or Flask. Additionally, AI is very expansive and has numerous applications in our world. If you’re interested in creating models that can play video games, check out OpenAI. If you want to make static images turn into goofy animations, check out the First Order Motion Model. Some of these algorithms can seem daunting and complex, but each subsequent project should help everything make more and more sense.”
