Agreed! These are great. More "Algorithmic Mental Models" for dynamic programming, backtracking, etc. would be extremely helpful. Thanks for the video!
I've never before seen a CS video that approached CS like math (i.e., here are the concepts, here are some keywords to look out for so you know when to apply these concepts, here are a few examples). The world needs 1,000 more videos like this (DP, Linked Lists, Trees, Graphs, etc). Fantastic work!
I knew about Sliding Window but never fully understood the pattern of questions to target or saw it visually represented so well and simply. You should definitely consider doing a playlist of these patterns, super intuitive.
Who even has the audacity to dislike such a video?? The best and simplest explanation of sliding window concept i have ever come across. Thank you sir!
I came across this video after struggling with sliding window problems in preparation for my upcoming Google interview, and just wanted to thank you for the super clear explanation. I'm feeling much more confident with these problems now. 👍
This is the best video I've ever seen on this topic "sliding window"! It does not only solve one or two interview questions for me but also solves a group of problems. More than that, it teaches me how to spot this group of problems. Fantastic!!!
Was struggling a lot with sliding window problems earlier, your explanation really simplified those problems. I really appreciate your effort. Thank you! Please post more videos.
Thank you @Ryan Schachte for clarifying the sliding window technique and make it so simple just like that. I hope you upload a lot more vids like this bro !
dude I am only 2 minutes in and I've liked and subscribed! Between the visuals and your consice explanations, the sliding window technique (which I never thought I'd understand) makes SO MUCH SENSE! Thanks so much :)
I have always been calculating the first windowSum in a separate loop before starting to "move" the window in a separate loop. I never knew you could do both within the same loop without using a nested loop. This is amazing!
This is fantastic! Please make more of these 'mental model' videos. There too many videos out there that jump straight to the solution without any discussion of how to approach and generalize a problem. We need more video's like this!
You might be a professor or a normal student, but your explanation skills are exceptional. Add to that most of the videos explaining algorithms do not include such an amazing visualization for the data structures that we are working with. I hope one day you will have the free time to make a full algorithms and data structures for competitive programming course with such an amazing visualization. Hope you the best sir whereever you are.
What an excellent, well paced, well explained video with explaining the theory but also showing it in examples THANK YOU! The more you research the more you come to understand that trying to solve all these problems individually with out knowing about techniques like these, is so painful lol
Great explanation! I'm a visual learner and I can see myself imagining these sliding window animations when I face array problems like this in the future.
After watching this one, I could solve even the hard LC problems within 15-20 mins. This is a gem of a video on this topic. Hope you make more of these on other topics, I will literally pay for it
31:11 The reason you "add 1" isn't because the array is indexed at 0. It's because the start element gets subtracted and it needs to be added back. Thank you for making the video :)
Max subarray sum of k is also a dynamic programming problem. You reuse the previous max result and update it. So it is like dynamic programming with space complexity O(1).
This is such an awesome video by abstracting the idea of sliding window to tackle a group of specific issues with similarities. Regarding the question of smallest subarray for a given target, I think we need to add the constraint that every element in the array is positive. Otherwise, the sum can be reduced even if we grow the size of the window without dropping the front one. 28:37
This is the best video I have ever seen on an algorithmic topic!! Please make more content like this!! Love the idea of the algorithmic mental model! :))
This video just came up in my recs as I'm interview prepping and I'm so mad this is your only video like it! I thought I hit a goldmine for interview prep for a sec LOL, but nonetheless this video is amazing. Thank you so much
In the second example 32:17 you're missing a condition in the inner while loop i.e windowStart should always be smaller or equal to windowEnd def get_smallest_sub_array_with_sum_greater_equal_to_a_value(a, k): left, right = 0, 0 min_window_size = float('+inf') current_sum = 0 while right < len(a): current_sum += a[right] while current_sum >= k and left
I have struggled with this concept for a really long time, but not anymore :) Your explanations were absolutely beautiful and I'm excited to solve some sliding window problems now! Can you please create more Algorithmic Mental Model videos? We'd all be super grateful!
OMG, Best problem solving explanation I've ever seen. it is a really helpful, simple, and to the point, Thank you for this extraordinary video. Liked and subscribed 🙌
Incredibly simple explanation. I felt like a baby being handed his lego building blocks. I absolutely learnt a hitherto abstract concept, i will never forget.
this was such a good explanation !! I hate Java but through your explanation I was able to understand what each line of code was doing. Even pointing out certain words that give away how one should solve the problem. Looking forward to future algo breakdowns !
Same! I know this is an old comment, but I was able to mentally translate this into the programming languages I do know algorithms in. This is a great video.
🎯 Key points for quick navigation: 00:09 *🧠 Sliding Window Technique Overview* - Overview of the sliding window technique as an algorithmic mental model, - Benefits of using sliding window over naive approaches, - Introduction to fixed size and dynamically resizing sliding window techniques. 02:00 *🪟 Fixed Size Sliding Window Technique* - Explanation of fixed size sliding window using a rectangular metaphor, - Example of maximizing sum of a contiguous subarray using fixed size sliding window, - Comparison with brute-force approach highlighting efficiency gains. 07:00 *📊 Identifying Sliding Window Problems* - Characteristics of problems suited for sliding window technique, - Common keywords and problem types (min/max, longest/shortest, containment), - Applicability across arrays, strings, and linked lists. 13:38 *🎯 Types of Sliding Window Problems* - Fixed length variant: Example with fixed size subarrays and maximizing sums, - Dynamic resizing variant: Handling variable window sizes dynamically, - Dynamic variant with auxiliary data structures: Examples involving hash maps for advanced constraints like distinct characters. 17:05 *🔄 Commonalities in Sliding Window Problems* - Sequential nature of problems and contiguous data groupings, - Criteria for solving problems (maximization, minimization, containment), - Unifying approach to algorithmic problem solving using sliding window technique. 22:16 *📊 Sliding Window Technique for Fixed Window Size* - Explanation of the fixed-size sliding window technique, - Detailed walkthrough of how to maintain and update the sliding window, - Example problem solved using the fixed-size sliding window approach. 24:58 *🎯 Dynamic Sliding Window for Variable Window Size* - Introduction to dynamic sliding window where window size can vary, - Problem-solving approach to find the smallest subarray with a given sum, - Demonstration of adjusting window size dynamically based on problem constraints. 33:03 *🧩 Sliding Window with Auxiliary Data Structure* - Application of sliding window technique with an auxiliary data structure, - Problem-solving for finding the longest substring with K distinct characters, - Strategy for using hash maps to optimize tracking of character frequencies in the sliding window. Made with HARPA AI
@Ryan : In case of smallestSubarray method, if targetSum is 9, it is failing, need to add another condition in it. private static int smallestSubarray(int targetSum, int[] input) { int currentWindowSum = 0; int minWindowSize = Integer.MAX_VALUE; int windowStart = 0; for (int windowEnd = 0; windowEnd < input.length; windowEnd++) { currentWindowSum += input[windowEnd]; while (currentWindowSum >= targetSum && windowEnd > windowStart ) { minWindowSize = Math.min(minWindowSize, windowEnd - windowStart + 1); currentWindowSum -= input[windowStart]; windowStart++; } } return minWindowSize; }
One of the best explanations of this concept. Please make some more "Algorithmic Mental Models" based videos.
dude u seriously need to make more of these. Ill pay for them.
Agreed! These are great. More "Algorithmic Mental Models" for dynamic programming, backtracking, etc. would be extremely helpful.
Thanks for the video!
it is not one of the best, it is the best
@@CEOofTheHood Yes. Please make more of these. Especially with your new freecodecamp traffic this would really take off.
Agreed
I've never before seen a CS video that approached CS like math (i.e., here are the concepts, here are some keywords to look out for so you know when to apply these concepts, here are a few examples). The world needs 1,000 more videos like this (DP, Linked Lists, Trees, Graphs, etc). Fantastic work!
I knew about Sliding Window but never fully understood the pattern of questions to target or saw it visually represented so well and simply. You should definitely consider doing a playlist of these patterns, super intuitive.
Who even has the audacity to dislike such a video?? The best and simplest explanation of sliding window concept i have ever come across. Thank you sir!
will it work if array has negative elements. I think no.
Only 219k views... no way. This is by far some of the best CS-related content on UA-cam, hands down. Amazing!!
Dude please create a playlist for the other techniques as well!!! This is gold!
Does he have a playlist for this?
I came across this video after struggling with sliding window problems in preparation for my upcoming Google interview, and just wanted to thank you for the super clear explanation. I'm feeling much more confident with these problems now. 👍
Did you get into google? How was the interview!?
This is really the best video (or any, really) explanation of the topic I have found. Really hope this would become more popular. Thank you!
This is the best video I've ever seen on this topic "sliding window"! It does not only solve one or two interview questions for me but also solves a group of problems. More than that, it teaches me how to spot this group of problems. Fantastic!!!
The best lecture on sliding window technique
This is the only video which made me understand dynamic window sizing algorithm... Thank you so much 🥰
Hands down the best explanation of the Sliding Window technique. Please do more algorithmic mental model videos!
I found "Algorithmic mental models" is a great concept for solving problems please bring more videos like this.
1. Liked 2. Subscribed 3. Notifications Turned On. Please keep posting, one of best the best explanations for sliding window problems. Thank You Ryan!
Was struggling a lot with sliding window problems earlier, your explanation really simplified those problems. I really appreciate your effort. Thank you! Please post more videos.
a simple visual explanation,
detailed, with variants
this is amazing, thank you
I've really struggled visualizing this algorithm concept and this video is extremely intuitive and detailed. Would love to have more of these videos.
Learning from you to for my job hunt in Zurich, I shall get back to this video once I get my job ! Thanks in advance for everything!
This is probably hands down the best explanation of the Sliding Window technique i've seen. Great job!
agree 100%!
One of the best tutorials I have even seen. Thanks.
Thank you @Ryan Schachte for clarifying the sliding window technique and make it so simple just like that.
I hope you upload a lot more vids like this bro !
The animation for second technique: dynamic SW is just awesome, I loved it
I like that you actually name your variables in a meaningful way. 👍
Wow great explanation, love the ‘teach a man to fish’ approach to the algorithm problems!
This is the best explanation on Sliding Window I have seen so far. Thank you for the detailed yet simple approach to explaining the concept.
why suddenly when i graduated all these channels poped up when i needed them the most , bro you are the best
I love the idea of "Algorithmic Mental Models"! Thank you so much for explaining this perfectly and I'd love to see more videos for this idea!
dude I am only 2 minutes in and I've liked and subscribed! Between the visuals and your consice explanations, the sliding window technique (which I never thought I'd understand) makes SO MUCH SENSE! Thanks so much :)
I have always been calculating the first windowSum in a separate loop before starting to "move" the window in a separate loop. I never knew you could do both within the same loop without using a nested loop. This is amazing!
This is fantastic! Please make more of these 'mental model' videos. There too many videos out there that jump straight to the solution without any discussion of how to approach and generalize a problem. We need more video's like this!
Nice explanation. I am glad I landed here while searching for all possible solutions for solving the array problems
Guys, seriously, just study this video! It will help tremendously!!!
Thanks for such helpful content
A series of algorithmic mental models would be awesome, great work!
You might be a professor or a normal student, but your explanation skills are exceptional. Add to that most of the videos explaining algorithms do not include such an amazing visualization for the data structures that we are working with. I hope one day you will have the free time to make a full algorithms and data structures for competitive programming course with such an amazing visualization. Hope you the best sir whereever you are.
What an excellent, well paced, well explained video with explaining the theory but also showing it in examples
THANK YOU!
The more you research the more you come to understand that trying to solve all these problems individually with out knowing about techniques like these, is so painful lol
Great explanation! I'm a visual learner and I can see myself imagining these sliding window animations when I face array problems like this in the future.
One of the The best videos to explain Sliding windows concept. Request you to make other such videos that will help in tech interviews.
we need more of this series! please do more!
Would love more of these 'Algorithmic Mental Models' videos. I'm sure everyone here would appreciate them.
@maryam I'm sure there are more capable people here who can help you. I don't have much experience with Python.
you are a very good teacher in layman language you are reaching. We will surely crack interviews with these
Thanks you with tear. You constructed a solid framework for handling sliding window in my head.
This is -by far- the best video on this topic I have seen. Thanks for taking the time to produce such a great content. Keep it up!
After watching this one, I could solve even the hard LC problems within 15-20 mins. This is a gem of a video on this topic. Hope you make more of these on other topics, I will literally pay for it
I'm absolutelly blown away how this is explained.
I struggled for longest substring problems and now here we are i solved it by my own after watching your video thanks buddy
31:11 The reason you "add 1" isn't because the array is indexed at 0. It's because the start element gets subtracted and it needs to be added back.
Thank you for making the video :)
One of the best explanation on YT
I am from India. THese are helping us cracking interview. THank you very much. Complicated topics you are making us understand very easily.
Great guy. I was hesitant to watch the whole video but you were so informative that I actually learned something for many use cases. Thanks
This video is amazing. hope you make more videos on "mental models" such as for dynamic programming.
Max subarray sum of k is also a dynamic programming problem. You reuse the previous max result and update it. So it is like dynamic programming with space complexity O(1).
wow this deserve to be a series very underrated!
This video gave the confidence that I'll mostly won't stuck in this topic anymore...Thank you : )
One of the best and easy explanations of this concept. thank you so much !
Literally the best explanation of sliding window technique.
This video is pure gold!!! Great explanation . Everything is very clear. Thank you very much!
I wouldn’t resist to pay a 1000$ to watch such videos. Brilliant piece of work! Hope you do more videos on “Mental models”
Please make more algorithm videos, you're literally the best explainer I have found on youtube.
This is such an awesome video by abstracting the idea of sliding window to tackle a group of specific issues with similarities. Regarding the question of smallest subarray for a given target, I think we need to add the constraint that every element in the array is positive. Otherwise, the sum can be reduced even if we grow the size of the window without dropping the front one. 28:37
this is the best video I was watching about sliding window - thank you !!
I can't thank you enough. I wish I have seen this video before I bombed in the Meta interview. Please make more videos on Algorithmic Mental Models!!!
Only 13 minutes in but this is beautiful so far. Thank you.
This is the best video I have ever seen on an algorithmic topic!! Please make more content like this!! Love the idea of the algorithmic mental model! :))
Rewatched some parts of it. Perfect, breathtaking. The best tutorial ever.
This video just came up in my recs as I'm interview prepping and I'm so mad this is your only video like it! I thought I hit a goldmine for interview prep for a sec LOL, but nonetheless this video is amazing. Thank you so much
More to come ;)
This is the best video for sliding window techniques
This is amazing. I wish I could hug you for this best explainantion. I learned a lot.
This is the best explaination that i have came across 🙂
In the second example 32:17 you're missing a condition in the inner while loop i.e windowStart should always be smaller or equal to windowEnd
def get_smallest_sub_array_with_sum_greater_equal_to_a_value(a, k):
left, right = 0, 0
min_window_size = float('+inf')
current_sum = 0
while right < len(a):
current_sum += a[right]
while current_sum >= k and left
I greatly appreciate the clear explanation of the dynamically resizable sliding window, it cleared up some doubts I have about the algorithm. Cheers!
the best lecture available on any platform for SWT.
I loved this video and your way of explanation.
This is the best video I have seen!!Please please please make more videos on Algorithm Models!!
I have struggled with this concept for a really long time, but not anymore :) Your explanations were absolutely beautiful and I'm excited to solve some sliding window problems now! Can you please create more Algorithmic Mental Model videos? We'd all be super grateful!
OMG, Best problem solving explanation I've ever seen.
it is a really helpful, simple, and to the point, Thank you for this extraordinary video.
Liked and subscribed 🙌
Nice video! Banger as a refresher for interviews.
Please make more videos like this, it's one of the best things I've seen on youtube
Best explanation so far. Thank you
I loved, looooved the format of this! Please continue to do these videos. Extremely helpful!!
Thank you for your help. Please add more videos to the algorithmic mental models series. please and thank you!
Amazing explanation of the Sliding-Window Technique. Would love to see more of Algorithmic Mental Models.
Thanks, I flew through LeetCode Sliding Window challenges after watching this.
I need more of this!! You just earned yourself a subscriber
Best video on youtube about sliding window technique. Thank you so much.
Incredibly simple explanation. I felt like a baby being handed his lego building blocks. I absolutely learnt a hitherto abstract concept, i will never forget.
Great use of graphics to explain the algorithm! :D
what a comprehensive video! Learnt a lot, liked and subbed. Keep bringing new content, you teach really good!
Great explanation, didn't know anything about sliding window before this video and I got enough information where I could implement it no problem.
This is a really great informative video, thanks for the hard work! I really like the breakdown to creating a mental model
this was such a good explanation !! I hate Java but through your explanation I was able to understand what each line of code was doing. Even pointing out certain words that give away how one should solve the problem. Looking forward to future algo breakdowns !
Same! I know this is an old comment, but I was able to mentally translate this into the programming languages I do know algorithms in. This is a great video.
Hand down, the best explanation. Great work🎉 thank you❤
Fantastic, you should seriously consider doing dynamic programming series. Just 36 mins but got avery clear understanding of the topic
Do you have a series for these Algorithmic Mental Models? Your video was extremely helpful and I would love to see more of these!
This is an awesome, thorough explanation of the sliding window concept and is easy to follow!
Best explanation so far. Please make more videos on algorithmns
Please make more of these! This was so helpful thank you.
Great explanation for the problems! Code is concise!
🎯 Key points for quick navigation:
00:09 *🧠 Sliding Window Technique Overview*
- Overview of the sliding window technique as an algorithmic mental model,
- Benefits of using sliding window over naive approaches,
- Introduction to fixed size and dynamically resizing sliding window techniques.
02:00 *🪟 Fixed Size Sliding Window Technique*
- Explanation of fixed size sliding window using a rectangular metaphor,
- Example of maximizing sum of a contiguous subarray using fixed size sliding window,
- Comparison with brute-force approach highlighting efficiency gains.
07:00 *📊 Identifying Sliding Window Problems*
- Characteristics of problems suited for sliding window technique,
- Common keywords and problem types (min/max, longest/shortest, containment),
- Applicability across arrays, strings, and linked lists.
13:38 *🎯 Types of Sliding Window Problems*
- Fixed length variant: Example with fixed size subarrays and maximizing sums,
- Dynamic resizing variant: Handling variable window sizes dynamically,
- Dynamic variant with auxiliary data structures: Examples involving hash maps for advanced constraints like distinct characters.
17:05 *🔄 Commonalities in Sliding Window Problems*
- Sequential nature of problems and contiguous data groupings,
- Criteria for solving problems (maximization, minimization, containment),
- Unifying approach to algorithmic problem solving using sliding window technique.
22:16 *📊 Sliding Window Technique for Fixed Window Size*
- Explanation of the fixed-size sliding window technique,
- Detailed walkthrough of how to maintain and update the sliding window,
- Example problem solved using the fixed-size sliding window approach.
24:58 *🎯 Dynamic Sliding Window for Variable Window Size*
- Introduction to dynamic sliding window where window size can vary,
- Problem-solving approach to find the smallest subarray with a given sum,
- Demonstration of adjusting window size dynamically based on problem constraints.
33:03 *🧩 Sliding Window with Auxiliary Data Structure*
- Application of sliding window technique with an auxiliary data structure,
- Problem-solving for finding the longest substring with K distinct characters,
- Strategy for using hash maps to optimize tracking of character frequencies in the sliding window.
Made with HARPA AI
Thanks a lot, great explanation. I have been looking for UA-camrs like you all the time, good explanations with good content :D
@Ryan : In case of smallestSubarray method, if targetSum is 9, it is failing, need to add another condition in it.
private static int smallestSubarray(int targetSum, int[] input) {
int currentWindowSum = 0;
int minWindowSize = Integer.MAX_VALUE;
int windowStart = 0;
for (int windowEnd = 0; windowEnd < input.length; windowEnd++) {
currentWindowSum += input[windowEnd];
while (currentWindowSum >= targetSum && windowEnd > windowStart ) {
minWindowSize = Math.min(minWindowSize, windowEnd - windowStart + 1);
currentWindowSum -= input[windowStart];
windowStart++;
}
}
return minWindowSize;
}
Best explanation so far
Great explanation and thanks a lot. The way you visualized really helped to understand the concept.