dr-neptune
Joined 4 years ago
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76
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31
Member since
Apr 18
Recent submissions
Comment · 3 years ago
Comment · 4 years ago
At the moment I'm really passionate about 2 things:
Quantitative Finance. I have a math background and relish the chance to explore some applications of probability theory. Most of my readings have been about options pricing since there is a lot of mathematics to explore there
My second recent passion is synthesizer. I got a hardware semi-modular synth last year and I've had a ton of fun patching with real wires, twisting knobs and tinkering with loops. As a bonus, it feels like programming but I'm able to be away from the screen while doing it
Comment · 4 years ago
Very cool. It's amazing how cutting edge rackets plotting facilities are
The rest of the blog has a bunch of inspiring content for the aspiring Racketeer that isn't catering to students or programming language theory researchers
Comment · 4 years ago
One way could be to time cap something and put it out there. Many may fail, but perhaps one will take off. Here is a blog where a dev makes many small startups and sees how they do: https://tinyprojects.dev/
Comment · 4 years ago
Another useful idea in this same quest is to keep an ideas file that is easy to update. Then hold yourself accountable to add at least 1 idea a day, no matter how silly.
Over time you will find yourself dredging up all kinds of ideas without meaning to (while doing other things) and before you know it you will have a long list that can then be prioritized and explored
Comment · 4 years ago
Fire can basically do the first step (object -> CLI):
https://github.com/google/python-fire
Gooey can do (CLI -> GUI):
Comment · 4 years ago
A book I always recommend to folks looking to learn mathematics with lots of beauty and from first principles is Serge Lang's Basic Mathematics.
The book starts from axiomatic arithmetic and works all the way up to what you would need as a mathematics major with a focus on pure mathematics. He also manages to touch some beautiful areas like geometry, abstract algebra, symmetry, linear algebra, set theory and more.
There are only a handful of exercises at the end of each section and they are very good at locking in the concepts. I finished a mathematics degree and realized afterwards that there just wasn't enough of a focus on the concepts and beauty. Even with 4 years of math experience, this book still managed to open my eyes
Comment · 4 years ago
Not sure what the parent uses, but emacs has calc, which is a RPN calculator with lots of features (including history and intermediate results) and can be used in the terminal
Comment · 4 years ago
Is it because you are used to vim bindings? Emacs offers an "evil mode" that implements modal bindings
Comment · 4 years ago
> By getting into machine or deep learning I mean building upto a stage to do ML/DL research.
> The target ability:
> 1. To understand the theory behind the algorithms
> 2. To implement an algorithm on a dataset of choice. (Data cleaning and management should also be learned)
> 3. Read research publications and try to implement them.
There are many different ways that people do ML/DL research these days. Some people do more theory-work which will necessarily be more focused on mathematics, and others do more of an applied approach which will be more focused on coding and iterating.
For theory-driven work, I think Michael I Jordans list is still pretty solid:
> https://news.ycombinator.com/item?id=1055389
I would focus on the fundamentals first though:
1. get a solid background in mathematics
- analysis (a suggestion is Baby Rudin)
- probability (Grimmet and Stirzaker, maybe something with measure theory after)
- statistics (Casella and Berger or Wasserman's book is a good start)
- Introduction to Statistical Learning is a fantastic start
- Then choose 1 or both of the following:
- Elements of Statistical Learning for a Frequentist Approach
- Pattern Recognition & Machine Learning for a Bayesian Approach
- the deep learning book by Goodfellow is decent
- start reading papers here and trying to implement them
If you want to go the academic route, you have enough of a view of the field to begin specializing further. Choose a sub-domain and dig deep if you want to do more deep learning work. Maybe revisit Michael I Jordan's list if you're still confused about where to go. A lot of those books will feel a lot more familiar.
Best of luck!
Comment · 4 years ago
duckduckgo also has "bang operators" that allow you to specify site. An example is !r for reddit or !w for wikipedia
It's even nicer for some sites like Wikipedia because it goes directly to the page if theres an exact match instead of the intermediate search results page
- grammarly (mostly excellent, I ignore some suggestions)
- reading books more with stylistic elements that push the boundaries of what you're used to. I write technical blogs, but I've found some inspiration reading very different writing. Some examples are Thomas Pynchon, James Joyce, Greg Egan
- setting up constraints. Can you explain this in less sentences without making a long run-on sentence? How concise can you make this whole post? On the flipside, can you avoid all the most common adjectives when describing something? If you take a tokenized word count on your work, what words do you rely on? Can you delete them?