My Theory of Learning Faster
Summary
TLDRThe speaker compares learning to coding to building neural circuits in the brain. They explain that mastering a skill like writing a DFS algorithm involves creating a mental circuit that becomes quicker to access over time. However, true understanding requires spaced repetition and tackling varied problems to strengthen the neural network. The analogy of machine learning's training phase is used to highlight the importance of practice and the risk of overfitting to one problem. The speaker emphasizes the need for consistent practice to maintain these mental circuits, drawing parallels to the 'use it or lose it' principle in both human learning and computer memory allocation.
Takeaways
- đ§ The human brain is like a neural network with circuits that form when learning new skills, such as coding algorithms.
- â±ïž Mastering a skill like writing a DFS algorithm becomes faster over time as the brain's 'circuit' for that skill strengthens.
- đĄ The initial learning phase is slow and requires effort, similar to the training phase in machine learning, which is computationally intensive.
- đ Spaced repetition is essential for solidifying learning and creating a robust neural circuit in the brain.
- đ Solving similar problems in a grouped manner helps reinforce learning by activating the same neural pathways repeatedly.
- đ The learning curve starts flat but with consistent practice, it leads to exponential growth in skill and understanding.
- 𧩠Each new concept or skill builds upon previous knowledge, creating a network of interconnected neural circuits.
- đ The 'use it or lose it' principle applies to neural circuits; without regular practice, the circuit weakens and may be lost.
- đ€ Overfitting to one problem is not ideal; the brain should be trained to generalize and adapt to variations of a concept.
- đ Regular practice of a skill, like solving coding problems, helps maintain and strengthen the associated neural circuitry.
Q & A
What is the analogy used to describe the learning process in the human brain?
-The learning process in the human brain is compared to a circuit or a neural network, where inputs and outputs are connected by a network of neurons that form when learning something new.
Why does the speaker claim to be able to write a DFS algorithm quickly?
-The speaker can write a DFS algorithm quickly because they have developed a 'circuit' in their brain for DFS through repeated practice, which has become efficient over time.
What is the 'circuit' in the brain that the speaker refers to?
-The 'circuit' in the brain refers to the neural connections and pathways that are formed and strengthened through learning and practice, which enable quick recall and execution of learned skills or knowledge.
How does the speaker describe the initial phase of learning something new?
-The initial phase of learning something new is described as slow and requiring concentration, as it involves the creation of new neural connections in the brain.
What is the significance of the phrase 'use it or lose it' in the context of the brain's learning process?
-The phrase 'use it or lose it' implies that if a learned skill or knowledge is not practiced and used regularly, the neural connections associated with it may weaken and eventually be lost, as the brain reallocates resources.
What is the role of spaced repetition in learning according to the speaker?
-Spaced repetition plays a crucial role in learning by allowing the reinforcement of neural connections through multiple exposures to the learned material over time, which helps in solidifying the 'circuit' in the brain.
Why does the speaker suggest solving similar problems grouped together?
-Solving similar problems grouped together helps in reinforcing the neural connections related to the learned concept, making it easier to recall and apply the knowledge in different contexts.
How does the speaker relate the learning process to machine learning?
-The speaker relates the learning process to machine learning by comparing the initial phase of learning, which is slow and effortful, to the training phase in machine learning. Both require effort to build connections or models, but once established, execution or application is much quicker.
What is the importance of not overfitting in the context of learning a new concept?
-Not overfitting in learning means not focusing too much on a single problem or aspect, which can limit the generalizability of the learned skill. It's important to expose oneself to a variety of problems to strengthen the neural connections in a flexible way.
Why does the speaker mention the concept of a learning curve when learning new things?
-The learning curve is mentioned to illustrate the initial flat phase of learning where progress seems slow, followed by exponential growth once a foundational understanding is established.
How does the speaker explain the difference between learning programming and learning math?
-The speaker explains that while there might be some overlap, the neural circuits developed for programming are very different from those for math, requiring a different kind of thinking and thus a new set of neural connections.
Outlines
đ§ Understanding the Brain's Learning Circuit
The speaker explains the process of learning through the lens of brain circuitry, comparing it to a neural network. They emphasize that learning a new skill, like writing a DFS algorithm, involves creating a 'circuit' in the brain. This circuit is slow to form and requires concentrated effort, but once established, it allows for quick recall and application. The analogy is made to machine learning, where the training phase is the most time-consuming. The speaker also points out that a single exposure to a problem is not enough for deep learning, advocating for spaced repetition to reinforce the neural pathways. They suggest solving similar problems in succession to strengthen the learning circuit, drawing a parallel to overfitting in machine learning where too much focus on one problem can hinder generalization. The importance of using the learned skill regularly is highlighted, using the 'use it or lose it' principle, which warns against the atrophy of unused neural circuits.
đ Applying Learning Principles to Enhance Efficiency
In the second paragraph, the speaker reinforces the idea that the principles discussed are not novel but are widely accepted understandings of how the human brain functions. They encourage the audience to apply this knowledge to learn more efficiently. The speaker also mentions their own practice of regularly solving problems, which helps them maintain their proficiency in writing algorithms like DFS. The paragraph concludes with a reminder that the information shared is intended to help viewers improve their learning processes, and it is not meant to be overly complex or esoteric.
Mindmap
Keywords
đĄDFS (Depth-First Search)
đĄNeural Network
đĄCircuit
đĄSpaced Repetition
đĄLeetCode
đĄOverfitting
đĄAlgorithm
đĄEfficiency
đĄUse It or Lose It
đĄMemory
Highlights
The human brain forms neural circuits when learning new skills, like writing a DFS algorithm.
Becoming proficient at a skill involves creating and strengthening neural connections over time.
The process of learning is slow initially as the brain builds the necessary neural circuitry.
Once a neural circuit is established, performing the learned task becomes much faster.
The analogy of machine learning's training phase is used to explain the intensive effort required for initial learning.
To solidify learning, one must practice the skill multiple times, akin to spaced repetition.
The importance of solving similar problems in succession to reinforce the neural circuit is emphasized.
The concept of overfitting in machine learning is compared to memorizing a single problem without understanding.
Learning new concepts is facilitated by relating them to existing knowledge, reducing the learning curve.
The use-it-or-lose-it principle is applied to neural circuits, where disuse leads to the loss of learned skills.
The brain's efficiency leads to the deallocation of unused neural resources, similar to a computer's RAM.
Regular practice is necessary to maintain the neural circuits associated with learned skills.
The speaker's ability to quickly write DFS is attributed to regular practice and problem-solving.
The process of learning is not a sudden breakthrough but a gradual, exponential growth over time.
The speaker clarifies that the concepts discussed are widely accepted understandings of how the human brain learns.
Efficient learning involves understanding the brain's mechanisms and applying them to practice and repetition.
Transcripts
I've solved a lot of leak code problems
I've gotten to the point where I can
probably write a DFS algorithm faster
than you can take a piss now why is that
am I just a genius no because this is
more than just about coding the human
brain is like a circuit a neural network
if you will there's inputs and then
outputs all the stuff that goes on in
between is the circuit that forms your
brain when you're learning something for
the first time you're actually literally
creating a little circuit in your brain
with little neurons and that circuit
tells your brain what to do so the
reason I can write DFS very very quickly
is because I have a circuit for DFS
literally physically in my brain the
problem is that building this circuit is
slow it takes time and sometimes you
have to concentrate really really hard
just to get one of these little neurons
in there and then form that connection
and then form another connection and
just sit there for minutes hours
sometimes days and then finally you have
this thing in your brain and the
Beautiful part is that once you have it
in your brain now the time comes to use
this circuit it goes very very quickly
oh which algorithm do I need to write
DFS boom it just went straight through
so just like when it comes to machine
learning the training phase is the most
timeconsuming part it's computationally
intensive it requires effort but once
it's there running through that neural
network is relatively quick so that's
how you learn but there's one caveat
just by doing something a single time
does not mean you have fully learned it
remember that time you solved a leak
code problem thought you understood it
and later tried to do it again but you
couldn't the reason is just because you
write an algorithm once does not mean
you have a deep understanding of it and
that's because you're a human not a
machine if you write DFS once you might
develop some of these nodes in this
circuit but I guarantee you won't have
every single one of them so next time
you try DFS you might get parts of it
correctly and maybe you will get the
problem correct but it might take you a
really long time to re-remember parts of
it so what's the solution to this do the
same thing multiple times AKA spaced
repetition this applies to more than
just leak code But continuing the
analogy that's why I created the N code
150 and then ordered the them in such a
way that you can solve similar problems
grouped together so anytime you're
trying to learn something make it easy
for yourself to do the same thing
multiple times in terms of coding solve
a problem and while it's still
relatively fresh in your mind how about
the next day ideally you can solve a
slightly different problem so then you
can kind of fire off different neurons
because we know there is a layer of
memorization brains can sometimes just
memorize things in terms of of machine
learning that would be considered like
overfitting for example you don't want
to like overfit too much for one problem
you want this circuit to be loose enough
such that it can be extended maybe you
see a slightly different problem and
during that problem you have this
network and then you realize oh actually
there is another possibility you go down
this path but then for the most part you
can kind of still connect to the rest of
the circuit you just had to create maybe
one new node this time it's not
impossible to create a couple new nodes
on the fly but it's very very hard to
create a brand new fully working circuit
on the Fly for example when you first
learned to program I was pretty good at
math but when I first learned
programming I was like what this is
completely different than any sort of
thinking I've ever done before I had a
bunch of circuits in there for math yeah
it might help you a little bit when it
comes to programming but this computer
science or programming circuit is very
very different from the math one even
though there probably is some overlap
then eventually it does start to get
easier because you have some sort of a
foundation and then you learn A New
Concept maybe even a new programming
language you have something to relate
this back to oh yeah Loops in this
language kind of similar to Loops in
another language one programming
Paradigm compared to another you need
some reference and that's why there is a
learning curve when learning new things
initially if we were to draw it out it
kind of looks like this it's a flat line
initially but eventually you do get that
exponential growth you just have to get
past the first phase of this part also
there's the use it or lose it principle
if you don't use a circuit inside of
your brain for a long time it's going to
start diminishing it's going to die off
this thing's dead that's dead because
your brain is efficient if I'm not using
these resourc ources for example memory
right like Ram it's kind of like a
computer I can deallocate this memory
and use it for something else why have
this memory used up if I'm not even
using it so maybe you learned DFS back
in college but it's been years and now
you forgot all of it and yeah that's
what happens with the brain the only
reason I can write it pretty quickly
even to this day is because I pretty
regularly solve these types of problems
on the YouTube channels to finish up I
want you to know nothing I said in this
video is rocket science it's not even
really my theory all of this stuff that
I've said is generally accepted this is
how the human brain works and when you
remember that that's when you can try to
learn more efficiently
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