How often to review your own notes
History / Edit / PDF / EPUB / BIB / 3 min read (~415 words)How often should I review my personal notes?
The simple answer is to do it as often as possible.
The more often you can go through your notes and review them, the more they will be fresh in your mind.
However, as you write more and more notes, it will be difficult to go through them all. At one point you will need to start prioritizing what you want to review and at what frequency. I see writing the same way I see programming: certain parts of the code work well and don't need to be touched for a while, so they are left alone. However, sometimes certain parts have become neglected with time and need to be looked at again.
All my notes on this blog, as well as my private project and personal notes are committed to git automatically every time I switch out of VS Code. This means that I have a very granular history of the last time a file was touched. This allows me to look back at the list of files I have and to find the files that haven't been touched in a while. Sometimes I'll read them again, think about them a bit, then close them if I have nothing to add. Other times I'll write about something that I've learned since then. Doing this feels a lot like improving some part of a codebase after you've learned how a piece of code would be used.
Another way you can schedule reviewing your notes would be with a spaced repetition system like Anki. It wouldn't be too much work to have a system that automatically creates an Anki note/card with a link to the file itself. You would click on the link, which would open the file in your text editor of choice, read the note, change it if needed, then close it. You would then go back to Anki and pick between hard/good/easy to decide when is the next time you would review the note.
I'd suggest dedicating a small amount of your week going through what you wrote and refreshing your memory. It is definitely useful to simply write things to get them out of your mind and to attempt to express your thoughts clearly. It's also useful to review what you thought about because your subconscious might have kept working on those thoughts well after you wrote them down and there might be more to contribute on a topic.
Why ask questions when few are answered
History / Edit / PDF / EPUB / BIB / 1 min read (~194 words)What is the point of asking ourselves many questions when we only answer a few of them?
The purpose of asking yourself questions is to prompt your subconscious to look for the answer.
Asking yourself questions is a way to determine if you know or do not know the answer. If you don't know the answer, then nothing will come on the top of your head quickly. On the other hand, if you know an acceptable answer, it will show itself rather quickly.
Asking yourself questions is a way to explore the domain of your knowledge and look for areas where you might need to do additional research.
If you ask yourself a lot of questions on a certain topic you're studying and you don't know the answers, then it can let you know that your knowledge is lacking and that you need to study more.
In some cases you might ask yourself questions that you don't really want to know the answer to.
One of the main reason a lot of questions remain unanswered is because asking questions is easy, but answering is hard.
What is belief traceability?
In software development, is it very common to want to have traceability between things. We want to know from where a requirement came from, who created the entry, who approved that it should be supported by the software, the issues in the issue tracker that will implement the requirement, who implemented the requirement, who reviewed the implementation, etc. The goal is to be able to retell the story of this requirement from beginning to end if necessary.
In the case of belief traceability, we want to be able to trace how we've come to believe in something. Being able to trace back to the source of our beliefs allows us to determine if we still want to believe in them. Being able to also determine how a belief affects or generates other beliefs can be quite critical when we are re-evaluating our beliefs at some points in our lives.
Belief traceability starts with making a list of all the beliefs you have. Once you have made this list, you can try to determine if certain beliefs lead to other beliefs. You can also try to figure out if you have conflicting beliefs. You can attempt to associate beliefs with people, either because you think they are the ones that led you to believe something, or because they believe in that belief themselves. You may also have changed beliefs at certain points in your life. As such, it is interesting to create a timeline of your beliefs, where said beliefs are seen as being replaced or evolving over time.
As you get familiar with the process of tracking your beliefs, you will want to update your belief tracking system as you acquire new beliefs. This will allow you to reflect explicitly on what you believe and help you make a thorough analysis of those beliefs.
Learning according to machine learning
History / Edit / PDF / EPUB / BIB / 2 min read (~201 words)What is learning according to machine learning?
It is (for supervised learning) looking at numerous samples, decomposing them into input variables and their associated target variable, and deriving according to an algorithm how to predict the target variable given input variables.
It is the (potentially lossy) compression of the observed samples, where the learning algorithm describes the compression/decompression algorithm. The compressed data is the information necessary for the algorithm to make predictions (decompression).
It is the creation of some "memory" of the observed samples. Whereas an untrained model has no memory of the dataset since it hasn't seen the data, a trained model has some form of memory. A simple model such as sklearn's DummyRegressor will learn and memorize the mean of the target variable. It may not have learned and memorized much, but it has built its internal model of the data.
It is to imitate as closely as possible the source of data it is trained on. This means that given input variables, it should produce target values that are as close as possible to those observed during training (learning).
Picking the best material to learn a topic
History / Edit / PDF / EPUB / BIB / 2 min read (~330 words)How does one determine the best material to learn a topic?
My approach has always been based on the wisdom of the crowd.
If I want to read a book on a given topic, I will first look for the books that are available and create a list. From that list I will then look at websites like goodreads to gather people's opinion on the book. I look for two factors: how many people read the book, and the overall rating of the book. The book should have been read by as many people as possible and have the highest rating. I will also read some of the low rating comments to get a sense of the negative feedback that has been provided on the book.
If I already have some knowledge about the topic I am studying, I might be looking for specific sub-topics to be covered. I would then inspect the table of contents of the books to determine if those sub-topics are covered. I may end up reading only a chapter of a book because it addresses something I'm interested in. The topic may also only be covered in this book.
In some cases, I will ask people that are more knowledgeable than I am to give me references. I use my knowledge of their expertise to determine the topics on which they can and cannot offer expertise and suggestions.
If there are multiple options of equally good books, then I will skim a few of them, maybe read a few passages to get a personal impression of the book. I prefer concise and non-repetitive content.
With my chosen book in hand, I will start to read through it from beginning to end. If at some point the content is less than ideal, I don't mind skipping it and looking for additional sources (e.g., books or websites) to fill those gaps.