armanboyaci
Joined 8 years ago
Karma
127
Submissions
48
Member since
Oct 3
Recent submissions
Comment · 1 year ago
Should we validate before we verify the software?
Comment · 1 year ago
What happens if you have a prior knowledge for $k$ as a probability distribution?
Comment · 1 year ago
>Being able to apply statistics is like having a secret superpower.
I totally with this sentence. BUT If you ask for my opinion, merely knowing a list of statistical formulas is not very helpful. Most of the time, people don’t remember the underlying assumptions, so there is a fair chance they will use them in inappropriate situations.
I recommend watching these two YouTube videos. The presenters advocate using simulation/bootstrapping/shuffling methods instead of memorizing formulas.
Jake Vanderplas - Statistics for Hackers https://www.youtube.com/watch?v=Iq9DzN6mvYA
John Rauser - Statistics Without the Agonizing Pain https://www.youtube.com/watch?v=5Dnw46eC-0o
Comment · 1 year ago
I believe your description of software development is highly aligned with the ideas of peter naur, programming as theory building.
Comment · 1 year ago
> I wish there was two types of ipynb files, one for file with just code and markdown (for example ipynbc), and one for keeping code+markdown+results.
I believe you can achieve that if you use jupytext library, right?
Comment · 2 years ago
I am keeping my CV on overleaf and it is very convenient. Did you try that option?
Comment · 2 years ago
https://www.amazon.com/Solution-Selling-Fieldbook-Practical-...
If you are planning to build enterprise software solutions then I highly recommend this book. It contains very helpfull checklists and templates.
Comment · 2 years ago
I recommend this blog: https://yetanothermathprogrammingconsultant.blogspot.com/?m=...
Not all posts are business related but you can learn many practical tricks hard to find in books.
Comment · 2 years ago
You can compute the max-flow of an undirected graph. The edges have capacities and in the undirected case you assume that capacity can be used in both 'directions'.
Comment · 2 years ago
I found this explanation: https://www.promptingguide.ai/techniques/rag
> General-purpose language models can be fine-tuned to achieve several common tasks such as sentiment analysis and named entity recognition. These tasks generally don't require additional background knowledge.
> For more complex and knowledge-intensive tasks, it's possible to build a language model-based system that accesses external knowledge sources to complete tasks. This enables more factual consistency, improves reliability of the generated responses, and helps to mitigate the problem of "hallucination".
> Meta AI researchers introduced a method called Retrieval Augmented Generation (RAG) to address such knowledge-intensive tasks. RAG combines an information retrieval component with a text generator model. RAG can be fine-tuned and its internal knowledge can be modified in an efficient manner and without needing retraining of the entire model.
Comment · 2 years ago
Very good and important observation. In his talk "Simple made easy" [1] Rich Hickey defines simple as opposite of complex and easy as opposite of hard.
The easiness is relative (as you described) and depends on the things you are familiar with. For example, Docker containers and k8s stuff is easy (for you), and GraphQL is hard (for you).
The simplicity should be assessed (somehow) more objectively.
Comment · 3 years ago
I am not sure but another reason could be the mortgage duration. In US 30-year is very common, according to my quick google search in Canada most mortgages are limited to a 25-year amortization period.
Comment · 3 years ago
You are right.
Comment · 3 years ago
Reminded me this blog post about the complexity cost of trying to cover all the edge cases, using the example of recipe writing!
https://buttondown.email/hillelwayne/archive/edge-case-poiso...