dr-neptune

Some things that have helped me:

- 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?

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

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

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/

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

Fire can basically do the first step (object -> CLI):

https://github.com/google/python-fire

Gooey can do (CLI -> GUI):

https://github.com/chriskiehl/Gooey

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

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

Is it because you are used to vim bindings? Emacs offers an "evil mode" that implements modal bindings

> 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)

2. get a solid foundation in statistical machine learning

  - 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

3. get a baseline understanding of deep learning

  - the deep learning book by Goodfellow is decent

  - start reading papers here and trying to implement them

If you get through to this last step, you are probably solid enough to get a job building models. If that's the route you want, then begin iterating on learning about new approaches in papers (look for papers with code / data) and implementing 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!

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

> They determined peak creativity by the point at which the subjects’ scientific papers had the most citations and were thus most influential

Wouldn't it be reasonable to assume that they are actually measuring popularity rather than creativity? I'd guess there is survivorship bias here since

- The study only looked at Nobel prize winners

- Experimental ideas tend to be niche, i.e. not applicable to most situations, not well explored already

Pretty excellent considering how many plots there are!

These days I hear a lot of "AIML"

It doesn't make much sense to me as I've never seen anyone use anything you'd find in an AI book that you wouldn't also find in a Machine learning book

For big data, I think that the terminology waned but data engineers internalized the desire to scale everything they make to handle big data. So data engineering teams are still using things like Spark (or databricks) even if their datasets aren't big enough to need that

In the book Hamming tasks the reader with creating in some detail a vision of your future. I did do this, but lacked the foresight to effectively find a way that I could shape my work time to be "Hamming" meaningful.

As an example from the book, Hamming tells a story in which he sits with a group of chemists at lunch. He then challenges them to come up with the most important problems in the field. They did so, and none of them were working on those problems. He ended up challenging them to explain why they had not sought to work on those problems, and was no longer invited to the lunch table.

In this case, Hamming's idea of a meaningful career is a really singular one: contribute to solving the most important questions in your given field. This worked out very well for him, and he claims to be an ordinary man who worked hard over a long period of time. For myself, I'm not sure what the important questions are quite yet :)

I second this -- fantastic book, and cheap too

I received it as a gift from a coworker and it was a delightful read. It was so inspiring that it made me consider seeking more meaningful work though

> Otherwise, the differences between the OS you'll find will be minor

I think this is by design. If things are too different from one os to another, it raises the barrier to entry and hampers adoption. Furthermore, Linux distributions often also rely on things like standardized tooling and file system layout to ensure backwards compatibility

There are people who try to do new things in the space (gnu guix and nixos come to mind), but they tend to be comparably fringe when put side by side with something more mainstream (and perhaps battle-tested) like Debian or BSD

Wishful thinking is mentioned several times in SICP, both in terms of to be implemented functions and in separation of concerns.

Oftentimes in the book, they will write out a function with reliance on a variety of other functions that haven't been written yet, but which show a blissfully declarative outline of exactly what the function does. Then you go and write the sub-functions.

Looking at the code after is quite nice, but it takes a bit to wrap your head around writing large swaths of code that can't run -- especially when you're used to writing REPL-driven code and consistently checking/"testing" it

This is a great suggestion

I've been reading the textbook for the intro macro class here and supplementing the reading with blogs and it seems to be working quite well. At the very least, I have a decent conceptual understanding and know the vocabulary after just a couple months of reading ~30m/day

Rob Hyndmans book is a great quick intro:

https://otexts.com/fpp3/

If you want to jump in the deep end (aka you know good chunk of mathematics), you could skim Hamilton's Time Series Analysis

A good way to learn is to do a bunch of time series analysis, kaggle has some