sage76
The takeaway for ordinary students is presumably: “Simply be an extraordinary mathematical talent who happens to live next door to a lonely 92-year-old legend.”
I have seen and experienced some real extremes there.
A solution set to the book Pattern Recognition and Machine Learning by Christopher Bishop.
It was almost like they required the standup as part of the process but never used it the way it should be used.
Remote: Yes
Willing to Relocate: Yes — ideally to Canada, US, EU or Australia
Technologies: PyTorch / NumPy / Scikit-learn / Math / AI/ML / Research
Résumé/CV: Available on request
GitHub: https://github.com/abhimanyu-jain/
Email: [email protected]
I’m a machine learning researcher and former software engineer, currently working independently on a deep dive into probabilistic models, inference, and machine learning theory — including a comprehensive solutions manual for Bishop’s Pattern Recognition and Machine Learning.
You can have a look at my progress here : https://github.com/abhimanyu-jain/PRML_Solutions
I’m looking for work where I can apply this theoretical depth — ideally roles involving ML research, prototyping, or model optimization — or contribute to academic or open-source projects with strong mathematical foundations.
I have a Master’s from Boston University focused on ML, and ~5 years industry experience (Python, backend). I’m especially interested in:
Algorithmic research and model development
Teams that blend theory and implementation
Teams working on open-ended problems
I’m flexible, fast-learning, and love pushing through complexity.
Location: India Remote: Yes Willing to Relocate: Yes — ideally to Canada, UK, US, or Australia Technologies: PyTorch / NumPy / Scikit-learn / Math / AI/ML / Research Résumé/CV: Available on request GitHub: https://github.com/abhimanyu-jain/ Email: [email protected]
I’m a machine learning researcher and former software engineer, currently working independently on a deep dive into probabilistic models, inference, and machine learning theory — including a comprehensive solutions manual for Bishop’s Pattern Recognition and Machine Learning.
You can have a look at my progress here : https://github.com/abhimanyu-jain/PRML_Solutions
I’m looking for work where I can apply this theoretical depth — ideally roles involving ML research, prototyping, or model optimization — or contribute to academic or open-source projects with strong mathematical foundations.
I have a Master’s from Boston University focused on ML, and ~5 years industry experience (Python, backend). I’m especially interested in:
Algorithmic research and model development
Teams that blend theory and implementation
Teams working on open-ended problems
I’m flexible, fast-learning, and love pushing through complexity.
One of those adaptations is cheating. I have seen really hardwokring students in grad school finally resort to cheating because the workload was insane, and they realized everyone else was cheating.
You could hire a parrot to do these jobs.
All these advice pieces are garbage. Easy to say, but then you put in thousands of hours of practice and dont seem to get any better.
I know a 50 year old personally who finished ironman triathlon.
Some books can take many many months to finish off like this, and most courses only cover a small percentage of the book.
One thing I do disagree with is the promotion of enlightened underperformers. Those guys seemed to always end up leaving and starting their own companies.
I think this is a lesson I am learning. I am going through the book PRML throroughly (which means attempting every single exercise) and writing out the solutions.
I also did the prereqs somewhat rigorously.
It has been incredibly time consuming.
Sama is also not technical.
'Get over it' is the most common response. That's why they shut up about it. People who are close to them are only close because of something positive that is being offered. Nobody wants to deal with anyone else's issues.
They seem to love "testing" how much they can bully someone.
I remember a few experiences where someone responded by being an even bigger dick, and they disappeared fast.
By reading papers and experimenting and trying things out? Throw in a few books for good measure?
Why are you answering the question you are asking yourself?