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on 30-Nov-2020 (Mon)

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Incremental reading works by breaking up key points of articles, often dozens or thousands of articles
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Incremental reading - Wikipedia, the free encyclopedia
s a method for learning and retaining information from reading that might otherwise be forgotten. It is particularly targeted to people who are trying to learn a large amount of information at once, particularly if that information is varied. <span>Incremental reading works by breaking up key points of articles, often dozens or thousands of articles, into flashcards, which are then learned and reviewed over an extended period. Concretely, when reading an article (in a web browser), the reader selects extracts (similar to underlinin




Flashcard 3404902305036

Question
In python, if you have sequence (i.e. list), named s, how would you get the value in that sequence with the minimum length (e.g. for s = ["one", "athree", "zero"], "one" would be returned)?
Answer
min(s, key=len)

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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Flashcard 6055815417100

Question
It is a software-assisted method for learning and retaining information from reading, which involves the creation of flashcards out of electronic articles.
Answer
Incremental reading

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill
Incremental reading - Wikipedia
sources. (January 2019) (Learn how and when to remove this template message) Using incremental reading with an Anki add-on: extracting a portion out of an article and creating a cloze deletion <span>Incremental reading is a software-assisted method for learning and retaining information from reading, which involves the creation of flashcards out of electronic articles. "Incremental reading" means "rea







The measure of meaning must involve the brain itself in addition to the information channel metric. Prior knowledge is essential in learning.
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Pleasure of learning - supermemo.guru
prelude to the reward that underlies the learn drive. Prior knowledge in information seeking We need to distinguish between information and meaning. Entropy is not a good measure of the latter. <span>The measure of meaning must involve the brain itself in addition to the information channel metric. Prior knowledge is essential in learning. Imagine that in your search for an interesting channel on the radio you find a news service. If the service is delivered in Thai and you do not speak Thai, you will prefer a service del




the hippocampus, is to light up in response to entropy, it must operate on the inputs from the entorhinal cortex (i.e. the input to the hippocampus itself). Those inputs will present the signal after a high degree of processing. Instead of pixels, it may present a concept. A high entropy signal at the sensory inputs will lose most of its noise component early in the process of neural selection, completion, and generalization.
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Pleasure of learning - supermemo.guru
brain cannot effectively detect the entropy of the signal hitting the retina or the eardrum. Like pixels of a monitor, retinal cells are not aware of what they display. If the detector, such as <span>the hippocampus, is to light up in response to entropy, it must operate on the inputs from the entorhinal cortex (i.e. the input to the hippocampus itself). Those inputs will present the signal after a high degree of processing. Instead of pixels, it may present a concept. A high entropy signal at the sensory inputs will lose most of its noise component early in the process of neural selection, completion, and generalization. The signal-to-noise ratio will determine how much information is lost. The bigger the noise, the bigger the loss. The smarter we are, the more selective this processing will be and the




the more selective this processing will be and the more information will be lost at that stage. That's good. We become blind to detail. Pattern recognition will act like a deterministic function, which by definition, results in a drop in entropy.
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Pleasure of learning - supermemo.guru
rocess of neural selection, completion, and generalization. The signal-to-noise ratio will determine how much information is lost. The bigger the noise, the bigger the loss. The smarter we are, <span>the more selective this processing will be and the more information will be lost at that stage. That's good. We become blind to detail. Pattern recognition will act like a deterministic function, which by definition, results in a drop in entropy. Complex patterns may become simple concepts. Those concepts will provide the actual input to the detector, e.g. the hippocampus. Note that the visual stream produced in experiments that




why both low and high entropy sensory signals can be uninteresting. After a degree of processing, a high entropy signal may lose all its noise and deliver a low entropy input to the hippocampus.
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Pleasure of learning - supermemo.guru
e scanned for surprisal and provide guidance to the entire learn drive system. This is why, in this case, the hippocampus appears to be responding to input entropy. The above reasoning explains <span>why both low and high entropy sensory signals can be uninteresting. After a degree of processing, a high entropy signal may lose all its noise and deliver a low entropy input to the hippocampus. We then observe the illusion of an "optimum entropy" level at sensory input. We need a new concept, learntropy, that will help us accurately determine the attractiveness of the signal.




For the same information and the same entropy level, we may accomplish highly different levels of signal attractiveness. There is always an optimum speed of delivery
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Pleasure of learning - supermemo.guru
rd to decode, as the high speed goes beyond our processing power. The same piece of music slowed down can quickly lose its appeal. The same happens in speech delivery or in classroom lecturing. <span>For the same information and the same entropy level, we may accomplish highly different levels of signal attractiveness. There is always an optimum speed of delivery and that speed depends on all other factors that power the learn drive, incl. prior knowledge. As such, speed of delivery is highly individual. I like to listen to lectures at 1.4x spee




Of course, while doing this, I'll constantly be looking up things in the docs, on StackOverflow, and so on. I'll also be reading and understanding pieces of the code I started from. It's tempting to Ankify all this, but it's a mistake: it takes too much time, and you Ankify too much that later turns out to be little use. However, when something is clearly a central concept, or I know I'll reuse it often, it's worth adding to Anki. In this way, I gradually build up a knowledge base of things I can use in real, live projects. And, slowly, I get better and better
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Once I'm making real progress on my project, and confident I've made a good choice of API, then it makes sense to work through a tutorial. I usually dip quickly into several such tutorials, and identify the one I believe I can learn most quickly from. And then I work through it. I do Ankify at this stage, but keep it relatively light. It's tempting to Ankify everything, but I end up memorizing lots of useless information, at great time cost. It's much better to only Ankify material I know I'll need repeatedly. Usually that means I can already see I need it right now, at the current stage of my project. On the first pass, I'm conservative, Ankifying less material. Then, once I've gone through a tutorial once, I go back over it, this time Ankifying everything I'm likely to need later. This second pass is usually quite rapid – often faster than the first pass – but on the second pass I have more context, and my judgment about what to Ankify is better.
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I continue doing this, bouncing back and forth between working on my project and working on Anki as I make my way through tutorials and documentation, as well as material that comes up while reading code – code from others, and even code I've written myself. I find it surprisingly helpful to Ankify the APIs for code I've personally written, if they're likely to be useful in the future. Just because I wrote something doesn't mean I'll remember it in future! So: don't jump into Ankifying tutorials and documentation straight away. Wait, and do it in tandem with serious work on your project. I must admit, part of the reason I advise this is because I find the advice hard to take myself. I nearly always regret not following it. I start a new project, think “Oh, I need such-and- such an API”, and then dive into a tutorial, spending hours on it. But I struggle and struggle and make very slow progress. Until I remember to find some working code to start from, and immediately find things are going much better. I then swear to never use the tutorial-first approach again.
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I've tried this a couple of times, and my advice is: don't do it. It's a form of a problem I described in the main body of the essay: the temptation to stockpile knowledge against some day when you'll use it. You will learn far more quickly if you're simultaneously using the API seriously in a project. Using the API to create something new helps you identify what is important to remember from the API. And it also – this is speculation – sends a signal to your brain saying “this really matters”, and that helps your memory quite a bit. So if you're tempted to do speculative Ankification, please don't.
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This is a difficult situation. I use the rule of thumb that if it seems likely I'm not going to use the API again, I delete the cards when they come up. But if it seems likely I'll use the API in the next year or so, I keep them in the deck. It's not a perfect solution, since I really do slightly disconnect from the cards. But it's the best compromise I've found.
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