Skip to main content

Phd is not enough

 A Stumbling upon "A PhD is Not Enough" was like discovering a hidden gem in a vast library. As I listened to Peter J. Feibelman's passionate narration, his voice resonated with raw authenticity, transforming the book into an engaging dialogue rather than a mere lecture. Each lesson echoed in my mind, urging me to rethink my approach to academia and beyond. 


7 enlightening takeaways:


1. Networking Matters. Feibelman emphasizes the importance of building relationships. Dynamic connections open doors, providing opportunities that a degree alone cannot.


2. Diverse Skills are Key. Beyond research, he highlights the necessity of acquiring skills in communication and leadership. These are essential for making an impact.


3. Persistence Pays Off. The journey in academia is riddled with challenges. His message encourages resilience, reminding us that triumph often requires relentless effort.


4. Seek Mentorship. Feibelman's portrayal of mentorship as a beacon of guidance reinforced its importance. Learning from others' experiences can shortcut our path to success.


5. Accept Failure. He doesn't shy away from discussing setbacks, teaching that they're stepping stones rather than barriers.


6. Stay Curious. His passion for lifelong learning ignites a fire within, urging us to remain inquisitive and open-minded.


7. Impact Over Knowledge. Feibelman stresses that knowledge should serve a purpose. Making a difference in the world is the true essence of our work.


Book/Audiobook: https://amzn.to/4evGAhq


You can access the audiobook when you register on the Audible platform using the l!nk above.

Comments

Popular posts from this blog

Python Road map

 

Is it the end of lora?

 Is this the end of LoRA as a fine-tuning approach? Singular Value fine-tuning is here! We have a new paper in town called the "Transformers Squared". It promises to adapt any LLM to any task without external intervention.  The core idea of the paper is to use Singular Value Decomposition (SVD) to factorize the weight matrices of transformers. During training, we learn different singular values for different tasks. More specifically, we learn to scale the singular values for different tasks. Tasks can be as diverse as math reasoning or coding. The possibilities are endless. During inference, we do a two-pass inference. In the first pass, the LLM decides which "scale" to use for which task. In the second pass, the LLM adapts itself to be a specialist in the task and responds to our prompt. How cool is that? Paper Title: Transformer2: Self-adaptive LLMs Paper: https://sakana.ai/transformer-squared Blog: https://arxiv.org/abs/2501.06252 Video Explanation: https://youtu...