My Brain after 569 Leetcode Problems

Summary

The speaker shares their journey of solving LeetCode problems, which eventually led to a Google job offer. They discuss the importance of starting with basic data structures and algorithms, progressing in a structured order, and the value of understanding rather than memorizing solutions. The video emphasizes the balance between practice and learning efficiency, suggesting around 150 well-understood problems are more beneficial than solving hundreds without review. It also touches on the practical applications of algorithms in industry and promotes a platform for tech career growth.

Takeaways

• 📚 The speaker started with easy coding problems and learned the basics of data structures and algorithms, which they believe is enough to start solving problems.
• 💡 They initially used C++ but later switched to Python for its concise code, even though they never used it in a job or internship.
• 🔍 The speaker found that the order in which problems are solved matters and created a roadmap to avoid common mistakes.
• 🚫 They advise against using verbose languages like Java for coding interviews due to time constraints.
• 🕒 The speaker emphasizes the importance of consistent practice over cramming, suggesting solving one or two problems a day.
• ⏱ They learned to recognize when to give up on a problem during an interview, aiming to solve medium problems within 20-25 minutes.
• 🤔 The speaker found that looking at solutions for complex problems can be efficient, as long as one understands why the solution works.
• 📈 They experienced an increase in problem-solving speed as they became familiar with common algorithms and patterns.
• 📊 The speaker believes that the key to passing tech interviews is the ability to apply algorithms to new problems quickly.
• 🔄 They highlight the importance of reviewing past problems for better retention of complex algorithms.
• 📉 The speaker warns of diminishing returns from solving too many problems and suggests focusing on understanding rather than quantity.
• 🎓 Lastly, they argue that while LeetCode isn't the most exciting, it helps with thinking about edge cases and unit testing, which are valuable in industry.

Q & A

What is the speaker's primary goal in solving LeetCode problems?

The speaker's primary goal is to prepare for technical interviews and eventually secure a job offer from a big tech company like Google.

Why did the speaker initially choose C++ for solving LeetCode problems?

The speaker chose C++ initially because it was the first language they learned, but later realized it was not the best choice for the interview preparation process.

What basic knowledge of data structures and algorithms did the speaker have before starting to solve LeetCode problems?

The speaker had basic knowledge of arrays, linked lists, hash maps, trees, sorting, and binary search before starting to solve LeetCode problems.

How many LeetCode problems did the speaker solve before getting an internship at a bank?

The speaker solved around 50 problems before getting an internship at a bank.

Why did the speaker switch to Python for solving LeetCode problems?

The speaker switched to Python because it was concise and had the shortest code length, and they did not need to learn the entire language, just the basic syntax and standard data structures.

What is the speaker's opinion on the importance of the order in which LeetCode problems are solved?

The speaker believes that the order in which problems are solved matters and that problems should be tackled in a specific sequence to build a solid understanding of different concepts.

What is the speaker's recommendation for the frequency of solving LeetCode problems?

The speaker recommends solving one or two problems consistently each day, as opposed to cramming, to ensure that the concepts stick.

What is the speaker's approach to handling problems they cannot solve within 45 to 60 minutes?

The speaker suggests that if they cannot solve a problem within 45 to 60 minutes, they look at a solution as long as they can understand why it works.

What is the speaker's view on the number of LeetCode problems one should solve to be prepared for interviews?

The speaker believes that quality is better than quantity and recommends solving about 150 questions, ensuring a deep understanding of each problem.

Why does the speaker consider reviewing previously solved LeetCode problems important?

The speaker considers reviewing important for the complex algorithms to sink in, as repetition is needed for the concepts to be well understood.

What is the speaker's stance on the usefulness of LeetCode for learning about application development?

The speaker believes that while LeetCode may not directly teach application development, it helps in thinking about edge cases and unit testing, which are applicable in industry.

What is the speaker's suggestion for those who feel they are not fully prepared for big tech interviews?

The speaker suggests that feeling 100% prepared is rare, and that one should aim to solve a medium problem in 20 to 25 minutes to have at least an 80% chance of passing most interviews.

Outlines

00:00

😀 Journey to Google Through LeetCode

The speaker shares their personal experience of solving LeetCode problems, which eventually led to an offer from Google. They discuss the importance of different levels of preparation and the process of interviewing candidates. Initially starting with C++ and later switching to Python for its conciseness, the speaker emphasizes the value of learning basic data structures and algorithms before diving into problem-solving. They also highlight the mistake of solving problems in a random order and the importance of tackling them in a structured sequence to build a solid understanding. The speaker shares their strategy of solving one or two problems daily for better retention and the realization that looking at solutions is not shameful if one can understand the underlying logic. This approach helped them to quickly increase their problem-solving skills and reach a point where they could solve complex problems efficiently.

05:01

😌 The Perpetual Preparation for Tech Interviews

In this paragraph, the speaker delves into the perpetual nature of interview preparation, particularly for big tech companies like Google or Facebook. They admit that feeling fully prepared is rare, as the unpredictability of interview questions makes it a probabilistic endeavor. The speaker suggests that being able to solve a medium difficulty problem within 20 to 25 minutes is a good benchmark for an 80% chance of passing most interviews. They also share their personal journey of solving up to 569 problems, not just for the challenge but to refine their skills. The speaker emphasizes the importance of quality over quantity in problem-solving and recommends revisiting and understanding each problem deeply. They propose creating multiple-choice quizzes as a method for efficient review and highlight the value of LeetCode in teaching edge case thinking and unit testing, which are applicable in real-world application development. The speaker also mentions the importance of continuous learning and improvement in the tech industry, suggesting platforms like Taro for career growth and professional development.

Keywords

💡LeetCode

LeetCode is an online platform used for practicing coding interview questions. It is central to the video's theme as the speaker discusses their journey of solving LeetCode problems to prepare for technical interviews. The script mentions solving 'leak code problems,' which is a colloquial term for LeetCode problems, indicating the platform's significance in interview preparation.

💡Google

Google is a leading technology company known for its rigorous interview process. In the script, the speaker mentions receiving an offer from Google after solving LeetCode problems and interviewing candidates, highlighting the company as a goal for those preparing for tech interviews.

💡Data Structures and Algorithms

Data structures and algorithms are foundational concepts in computer science, essential for solving coding problems. The speaker initially lacked expertise in these areas but emphasizes their importance in understanding and solving LeetCode problems, as they form the basis for tackling interview questions.

💡Edge Cases

Edge cases refer to the extreme, minimum, or maximum values in a given situation that could potentially cause a program to fail. The script mentions the speaker getting 'tripped up by edge cases,' illustrating the importance of considering all possible scenarios when solving coding problems.

💡Dynamic Programming

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. The speaker admits not knowing what dynamic programming was initially but later recognizes it as a high-value algorithm essential for solving certain LeetCode problems.

💡Python

Python is a programming language known for its readability and concise syntax. The speaker switched to Python for solving LeetCode problems because of its brevity and ease of learning, which contrasts with more verbose languages like Java, and highlights the language's utility in interview settings.

💡Sequential Questions

Sequential questions on LeetCode refer to a series of problems that build upon each other in difficulty. The speaker advises solving these in order to progressively develop a deeper understanding, using 'House robber' as an example of such a series.

💡Backtracking

Backtracking is an algorithmic technique for solving problems incrementally by trying to build a solution and undoing ('backtracking') steps that do not lead to a valid solution. The speaker mentions needing a solid understanding of backtracking before attempting dynamic programming problems on LeetCode.

💡Algorithm Patterns

Algorithm patterns are common approaches or templates used to solve specific types of problems. The speaker discusses learning and recognizing 99 patterns needed for solving LeetCode problems, indicating the importance of pattern recognition in efficient problem-solving.

💡Interview Preparation

Interview preparation in the context of the video refers to the process of equipping oneself with the necessary skills and knowledge to succeed in technical job interviews. The entire script revolves around the speaker's experience and advice on how to prepare effectively using LeetCode.

💡Taro

Taro is a platform mentioned in the script that helps individuals grow in their tech careers, offering master classes and resources for career advancement. It is used as an example of additional resources beyond LeetCode that can aid in interview preparation and professional development.

Highlights

The speaker has solved numerous LeetCode problems and eventually received a job offer from Google.

After interviewing over a dozen candidates, the speaker learned about different levels of preparation for coding interviews.

The speaker initially started with C++ for LeetCode but later realized it might not be the best choice due to verbosity.

Having basic knowledge of data structures and algorithms is sufficient before starting to solve LeetCode problems.

Solving 50 problems was enough for the speaker to secure an internship, but further improvement was necessary for big tech companies.

Switching to Python for LeetCode was beneficial due to its concise syntax, even without prior experience.

The speaker advises against using verbose languages like Java for LeetCode problems.

The speaker emphasizes that problem-solving speed in an interview is less important than the ability to solve the problem correctly.

LeetCode questions should be solved in a specific order to build a solid understanding of algorithms progressively.

Consistently solving one or two problems a day is more effective than cramming for LeetCode preparation.

The speaker suggests that if one cannot solve a problem within 45 to 60 minutes, it's unlikely they will solve it on their own.

There's no shame in looking at a solution for a LeetCode problem as long as one understands why it works.

The speaker created a roadmap to guide the order of solving LeetCode problems to avoid common mistakes.

The speaker was able to solve 250 LeetCode problems faster than the first 100 due to learning patterns and tricks.

Most LeetCode problems fall into specific buckets of algorithms, which can be mastered with practice.

The speaker suggests that being able to solve a medium problem in 20-25 minutes indicates readiness for tech interviews.

The speaker solved up to 569 LeetCode problems, emphasizing the importance of quality over quantity in problem-solving.

Repetition and review of previous problems are crucial for mastering complex algorithms.

LeetCode may not teach everything needed for application development, but it helps in thinking about edge cases and unit testing.

The speaker mentions Taro, a platform for tech career growth, as a resource for further learning and development.

Transcripts

I've solved a lot of leak code problems

hey guys I'm still unemployed so let's

solve another leak code problem and

eventually got an offer from Google

where I've interviewed over a dozen

candidates after hundreds of hours of

grinding and explaining problems I

learned there are different levels of

preparation and I'll share everything I

know about the process including how to

practice how many problems you should

solve common mistakes to avoid and

whether I think leak code is a total

waste of time like most people I started

with easy questions I used C plus plus

because it was the first language I

learned but this was a bit of a mistake

and unfortunately I didn't learn so

until later on initially I wasn't even

able to solve the easy problems even if

I knew the solution I would always get

tripped up by edge cases I wasn't an

expert in data structures and algorithms

I mainly knew the basics like arrays

linked lists hash Maps trees sorting and

binary search and honestly I think

that's pretty much enough before you

actually start solving problems because

a lot of the easy problems will solidify

your understanding of the basics and 50

problems later that that's where I was I

could solve a lot of the trivial

problems like reversing a string or

inverting a binary tree but I hadn't

even touched a graph problem and I

didn't even know what dynamic

programming was but believe it or not 50

problems was enough for me to get an

internship at a bank but I knew I had to

keep grinding if I wanted to get a job

at a big tech company so I continued to

solve problems in a random order which

was another mistake but probably 60 or

70 questions and I switched to python I

would always look at the leak code

discuss section and I saw that python

code was always the shortest and most

concise even though I had never used

python before and still to this day I've

never used python at any job or

internship it was an easy choice to make

because you don't have to learn the

entire language you really just have to

learn the basic syntax how to write if

statements for loops and all the

standard data structures and methods and

you can actually learn these as you

solve leak code problems just by

Googling as needed now C plus isn't a

bad choice either but I would definitely

avoid a verbose language like Java the

language probably won't be be a deal

breaker but it can definitely save you a

few minutes when you're typing out your

code which can definitely make a

difference in a real interview but it

doesn't matter how fast you type if you

can't actually solve the problem and

even after solving a hundred problems

while I had learned all the high value

algorithms like DFS BFS sliding window

and backtracking I wasn't able to

consistently solve a new problem for the

first time I didn't know it then but the

reason was that a lot of leak code

questions are sequential you've probably

seen questions like House robber 1 House

robber 2 and House robber 3. obviously

you should solve these questions in

order but actually most of leak code

questions should be solved in a specific

order for example you should never try

solving dynamic programming problems

unless you already have a solid

understanding of backtracking and you

probably shouldn't try solving graph

problems until you have a solid

understanding of trees this is obvious

in hindsight but I'm sure a lot of

people make this mistake which is why I

created a road map so that you don't

make the same mistake I did it turns out

that the order you solve problems in

does matter I also think that solving

one or two problems a day consistently

is a lot better than cramming because

the ideas will actually stick if you

need a little motivation I've been

solving daily lead code problems on my

second Channel as I continued I realized

that if I couldn't solve a question in

45 to 60 Minutes I wasn't going to be

able to solve it myself previously I

would literally bang my head against the

wall for three to four hours on a single

problem because I thought that was the

only way to learn but let's just be

honest most of us are not going to be

able to come up with some of these

complex academic algorithms by ourselves

and even if you can come up with Floyd's

tortoise and hair algorithm it's

probably not an efficient use of your

time there's no shame in looking at a

solution as long as you can understand

exactly why it works that's why in my

videos I really try to explain my

thought process behind every problem

this ended up saving me a lot of time

because I would get to a really hard

problem a crackhead problem I like to

call them like burst balloons and I

would get pretty close to solving the

problem I would know it's a dynamic

programming problem but I just couldn't

figure out the trick behind it then I

would look at a solution and only after

five minutes of reading I would realize

oh that's the trick and then I would be

able to immediately solve the problem I

was able to go from 100 to 250 solved

questions a lot faster than I was able

to solve my first 100 questions and by

then I had learned 99 of the patterns

that I needed even some of the complex

ones and I got to the point that writing

out these algorithms like DFS BFS and

binary search was as easy for me as

writing a for Loop even some Advanced

ones like Union find dixtras I could

write out in less than five minutes and

that's when I realized that most leak

code problems fall into these buckets

for medium questions you just need to

figure out what algorithm you need and

then apply it hard questions are similar

except there's usually a really complex

trick behind them and sometimes unless

you've seen it before only God can help

you figure it out or maybe your

interviewer if they happen to be

generous at this point I was confident I

could pass interviews but I wasn't 100

sure if I was ready for the big Tech

interviews like Google or Facebook and

spoiler alert you will probably never

feel a hundred percent prepared it's not

a binary decision whether you are ready

for an interview or not because you

never know which questions you're going

to be asked it's more of a distribution

you might have a 50 chance of passing or

you might get to the point where you

have a 90 chance of passing if you're a

perfectionist I'm sure you want 100

chance but that's almost never gonna be

the case I think a good rule of thumb is

that you should be able to solve a

medium problem that you've never seen

before in 20 to 25 minutes if you can do

that you probably have at least an 80

chance of passing most Fang interviews

and I continue to solve up to 569

problems even though I had already

gotten into Google mostly because I'm a

loser but it's not quite as time

consuming as you might think because

I've gotten to the point that I can

solve most medium questions in 10 to 15

minutes but I definitely don't recommend

it because there are diminishing returns

on solving more and more problems

problems I think quality is much better

than quantity I recommend solving about

150 questions like the neat code 150

list which has video Solutions and code

Solutions in 14 languages but make sure

you really understand each and every

problem a huge mistake that most people

make is that they don't go back and

review previous problems for these

complex algorithms to really sink in you

need repetition that's why I'm working

on taking each problem from the neat

code 150 list and converting it into a

multiple choice quiz because it's really

hard to go back and resolve each

question and usually all you need to do

is review the trick so I made quizzes

that you should be able to solve in a

few minutes but also get a lot of value

from it you can check out a handful of

problems right now but when the list is

complete ideally you should be able to

go through it in just a single day I

think this will be a huge Time Saver for

people so after all that is leak code

just a big waste of time yes if you're

trying to learn about development and

actually building applications lead code

probably isn't going to help you do that

but people will spend thousands of hours

getting a CS degree to get a decent

paying job but won't spend an extra 100

hours to get a big compensation bump it

doesn't really make sense to me don't

get me wrong leak code isn't the most

exciting thing in the world but I do

think it helps you think about edge

cases and how you would unit test your

code these are skills that can be

applied to application development and

believe it or not algorithms and data

structures are widely used in industry

from database engines to compilers but

it's true that leak code won't teach you

everything that's where Taro comes in

it's a YC backed platform to help you

grow in your Tech Career including

helping you onboard as quickly as

possible and get you a faster promotion

using master classes with experienced

tech leads and leaders you can check it

out using the link in the description

and use code neat code for 20 off

premium thanks to Taro for sponsoring

this video and I'll see you guys soon