My Theory of Learning Faster

Summary

The 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

• 🧠 The human brain is like a neural network with circuits that form when learning new skills, such as coding algorithms.
• ⏱️ Mastering a skill like writing a DFS algorithm becomes faster over time as the brain's 'circuit' for that skill strengthens.
• 💡 The initial learning phase is slow and requires effort, similar to the training phase in machine learning, which is computationally intensive.
• 🔄 Spaced repetition is essential for solidifying learning and creating a robust neural circuit in the brain.
• 🔗 Solving similar problems in a grouped manner helps reinforce learning by activating the same neural pathways repeatedly.
• 📈 The learning curve starts flat but with consistent practice, it leads to exponential growth in skill and understanding.
• 🧩 Each new concept or skill builds upon previous knowledge, creating a network of interconnected neural circuits.
• 🕒 The 'use it or lose it' principle applies to neural circuits; without regular practice, the circuit weakens and may be lost.
• 🤔 Overfitting to one problem is not ideal; the brain should be trained to generalize and adapt to variations of a concept.
• 🔄 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

00:00

🧠 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.

05:02

🎓 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.

Keywords

💡DFS (Depth-First Search)

DFS, or Depth-First Search, is a fundamental algorithm in computer science used for traversing or searching tree or graph data structures. In the context of the video, it is used as an example of a concept that, once learned and internalized, can be executed quickly by the brain due to the neural pathways formed. The video script mentions that the speaker can write a DFS algorithm quickly because they have a 'circuit' in their brain for it, illustrating how practice and repetition can lead to efficient execution of learned tasks.

💡Neural Network

A neural network is a series of algorithms modeled loosely after the human brain. It is designed to recognize patterns. In the video, the human brain is likened to a neural network, where learning is described as creating a 'circuit' of neurons that fire together to perform a task or solve a problem. The video uses this analogy to explain how repeated practice strengthens these neural pathways, making the execution of learned tasks faster and more efficient.

💡Circuit

In the video, 'circuit' is used metaphorically to describe the neural pathways in the brain that are formed when learning a new skill or concept. The speaker explains that when learning something for the first time, the brain is creating a 'circuit' with neurons that will facilitate the performance of that task in the future. This concept is central to the video's message about the importance of practice and repetition in learning.

💡Spaced Repetition

Spaced repetition is a learning technique that involves reviewing information at increasing intervals over time, which is proven to enhance long-term memory. The video emphasizes the importance of spaced repetition for solidifying neural pathways associated with learned tasks, suggesting that revisiting problems or concepts at different times can strengthen memory and understanding.

💡LeetCode

LeetCode is an online platform used for practicing coding challenges and is often used by programmers to prepare for technical interviews. In the script, LeetCode problems are used as examples of the types of problems that can help in creating and strengthening neural 'circuits' in the brain through practice.

💡Overfitting

Overfitting in machine learning occurs when a model learns the detail and noise in the training data to the extent that it negatively impacts the model's performance on new data. In the video, overfitting is used as an analogy to explain the importance of not just memorizing a single problem or solution, but rather understanding the underlying concepts that can be applied to a variety of similar problems.

💡Algorithm

An algorithm is a set of steps or rules used to solve a problem. In the video, algorithms like DFS are used to illustrate the process of learning and how the brain forms neural pathways to execute these algorithms efficiently. The video suggests that with practice, algorithms can be executed quickly, much like how a trained neural network processes information.

💡Efficiency

Efficiency in the context of the video refers to the ability to perform tasks or solve problems quickly and with minimal effort once the necessary neural pathways in the brain have been established. The video argues that while building these pathways can be slow and require effort, the payoff is increased efficiency in executing learned tasks.

💡Use It or Lose It

The 'use it or lose it' principle suggests that skills or knowledge that are not used over time can be lost. In the video, this principle is applied to the neural pathways in the brain, indicating that if a learned skill or concept is not practiced, the brain may start to diminish those pathways, leading to遗忘 of the skill or concept.

💡Memory

Memory, as discussed in the video, is the cognitive process of retaining and recalling information. The video uses the concept of memory to explain how the brain stores and retrieves information, comparing it to computer memory (RAM) and emphasizing the importance of regular practice to maintain the neural pathways associated with learned tasks.

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