As a Computer Science student, I’m always on the lookout for interesting projects to work on in my free time. I’ve tried to avoid tutorials and formulaic approaches, instead opting for inspiration from ChatGPT’s research tool, Medium articles, and YouTube videos. I’ve also browsed forums and explored fine-tuning models related to speech and language, particularly to assist non-native speakers with their pronunciation in English and Mandarin.
But I’ve started to feel like I’m hitting a plateau in this niche. I want to branch out into other areas of machine learning and speech processing. My current project, which involves transcribing audio and analyzing performance, feels more like software development than machine learning.
I know that this task leans more towards linguistics and sound engineering, but there are definitely overlaps with machine learning. Still, I want to explore other areas of the field that excite me.
I’m open to rebuilding existing papers to learn, but I want to ensure that I’m developing my skills in a way that allows me to modify and expand upon them. If you’re like me, struggling to find inspiration in machine learning, here are a few tips that have helped me:
## Explore different sources of inspiration
From research tools to Medium articles, there are plenty of sources of inspiration out there. Don’t be afraid to explore and find what works for you.
## Identify your interests
What areas of machine learning interest you the most? For me, it’s speech and language processing. Identify what drives you and focus on that.
## Don’t be afraid to branch out
Machine learning is a vast field, and there’s always room to explore new areas. Don’t be afraid to try something new and step out of your comfort zone.
## Practice and learn from others
Rebuilding existing papers and learning from others in the field is a great way to develop your skills. Don’t be afraid to reach out to others for guidance and advice.
## Stay pragmatic, but dive into theory when needed
While it’s important to stay focused on practical applications, don’t be afraid to dive into theory when needed. It’s essential to understand the underlying principles of machine learning.
I hope these tips have been helpful. If you have any suggestions or advice on finding inspiration in machine learning, I’d love to hear them.