تدريب Shadowing: The BEST Coding Interview Roadmap in 2023 (free) - تعلم التحدث بالإنجليزية عبر الفيديو
جارٍ إنشاء الدرس...
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So I created a leak code roadmap and made it free for everyone.
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But before I show you, make sure to like and subscribe.
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Nevermind, let me just show you.
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It's sort of like a non -linear video game where you kind of have options of
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which order you want to complete each topic in.
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Except, of course, this is leak code, so it's definitely not going to be as fun.
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But the reason I made this is that I've kind of had a visual map of this in my head.
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I kind of know the relationship between these topics.
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Like, for example, linked lists are kind of your introduction to graphs, where you have nodes and edges, and then right after that you learn about trees.
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Most people will learn this in a computer science course.
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But there's definitely a lot of topics here you probably won't learn in class, things like tries, which naturally come after trees.
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But remember that trees also cover binary trees, and before you learn about binary trees, I think it makes sense to learn about binary search
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because it's kind of the same algorithm that you use binary trees for.
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I've been wanting to make this for a very long time, but I just finally had time during the holidays.
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It feels good to have a visualization for my thoughts, but I think also this will help a lot of people.
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Whether you're a beginner being introduced to these topics, the first topic is arrays and hashing.
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You don't really need to know a lot of data structures and algorithms to solve this topic.
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You just need to have programming knowledge about loops and conditionals
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and of course basic data structures like arrays and hash maps
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but you don't really need to know how they're implemented
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and after that you learn more complicated things but you do
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so in a very structured way and
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if you're completely brand new to data structures
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and algorithms you can always check out my courses
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and get lifetime access to them we recently started adding articles
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to complement all of the video solutions that's my short little plug but moving on, I consider most of this map pretty objective.
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I think most people would agree that two -pointers should come before binary search, because binary search is sort of a specialization of two -pointers.
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The same thing could be said about sliding window, and similarly linked list, a lot of these linked list problems require two -pointers,
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so I think it makes sense to learn two -pointers before that.
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And of course, trees should come before linked lists.
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It's a more complicated version of a linked list.
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And then right after trees, things get pretty open ended.
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Like I said, we have tries, but we also have heaps, which are a specialization of binary trees.
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They are also a bit more complicated, I think.
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And we also have backtracking, which I think makes a lot of sense to learn after trees, because backtracking is essentially just a big decision tree.
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Like looking at one of these thumbnails, you can kind of see it here backtracking problems are naturally suited to trees, at least in terms of visualization.
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And then after backtracking things get even more open -ended, we move on to graphs, which we already kind of learned about with trees and linked lists.
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And many graph problems require recursive backtracking, usually depth -first search, so I think it makes sense to do it in this order.
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Also, we have one -dimensional dynamic programming.
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Dynamic programming is kind of a specialization of backtracking where you add caching and then, you know, there's the true dynamic programming solution,
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which looks a bit more concise, something like this.
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And that usually doesn't involve recursion, but I still think learning backtracking and getting good at it before tackling dynamic programming will make it a lot easier.
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And the reason I have two -dimensional dynamic programming after graphs,
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of course, we have it after one -dimensional dynamic programming because two -dimensional dynamic programming problems typically involve graphs.
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I think unique paths is one that does involve like a two -dimensional graph, longest increasing path in a matrix.
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You can imagine that's also going to be related to graphs.
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But we also have advanced graphs, which are kind of more academic related algorithms.
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Things like Dijkstra's algorithm with network delay time, you'll need a Dijkstra's algorithm.
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And that algorithm also involves a heap.
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It's the shortest path algorithm.
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So it makes sense to have advanced graphs come after the
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heaps category at this point i'm sure you're starting to get
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an idea of what i'm talking about all these topics are related to each other
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and learning it in that way can kind of help you solve individual problems
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if you couldn't solve a problem in one of these complicated
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categories there might be a reason for it might be
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because you're lacking in one of the more simple categories
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and it can be very difficult to figure that out
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because as you're just going through a single big list of leak code problems in a random order, you don't really know that.
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It's hard to figure out what you're good at and what you're not good at, and what you might need to practice more on.
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And some of these, I will admit, are a bit opinionated.
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Things like intervals.
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Why would you need to learn intervals after you learn about heaps?
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Well, there's not necessarily a good reason, though interval problems typically require sorting, which is different from heap sorting.
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But the main reason I put this here after heaps is because, first of all, intervals are probably a bit less important than heap problems,
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but also because one of these problems does require a heap but most of them don't.
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So you could, you know, make an argument that some of this list could be a little bit different.
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You could say the same thing about these greedy problems.
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Definitely not all of these greedy problems will require a heap.
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I think most of them will not.
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But still, I think greedy problems are less important than heap problems.
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Then last couple ones, we have bit manipulation.
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Again, I put this after two -dimensional or one -dimensional dynamic programming because one of these I think does involve dynamic programming.
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But other than that, there's no reason that you should learn bit manipulation after dynamic programming.
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But I do think it's generally less important than dynamic programming, so it makes sense.
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And math and geometry, though, I think this one makes a bit more sense because you should learn this after you learn graphs, because a lot of these are related to matrices.
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And some of these also do involve bit manipulation.
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I think these three do.
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But I guess if I wanted to make this map as accurate as possible, instead of having a topic like arrays and hashing, and then right after that,
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we have new topics like two -pointers.
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Does it really make sense for somebody to have to solve all of these problems, even some of the more difficult ones, before moving on to some easy two -pointer problems?
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Like some of these are pretty easy.
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I would argue definitely not.
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So I'm also thinking about creating a new version of this graph that's much more detailed.
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Like essentially we'll have multiple nodes for two -pointers.
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We'll have multiple blocks and maybe one of them will be the easy two -pointer block
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and then after that you can solve easy binary search problems.
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And then maybe after that, you can solve medium two -pointer problems.
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But if I did that, you can tell that this graph would have been ginormous.
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It's already pretty big.
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So I wanted to first start out with like a simple version.
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But if people are interested in me creating a more complex one, that's a bit more granular, I'm happy to do that.
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But can you believe that this is free?
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Every single one of these, when you open it up, it has a list of problems.
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Every single one of these problems has a video solution that I created on YouTube.
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It also has code in multiple languages not just python
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but java javascript and c plus plus we're working on adding even more than
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that feel free to contribute on github i also added a
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few more quality of life features things like being able to
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sort by the problem name also by the difficulty things
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that people have been asking for a long time you can also star problems some people were asking for
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that so feel free to you know star all the problems you want also you have to admit
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that it's kind of neat that this is a graph when we're learning about data structures and algorithms which, you know, graphs, you know, you know what I'm talking about.
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And, you know, you can play around with this.
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You can enable dragging, which will let you kind of move these around.
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But, you know, if you do that and you let go of it, it's going to end up opening the panel.
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Sorry if that's annoying.
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And yeah, I mean, did I mention that this is completely free?
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So definitely try it out.
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If you want to do me a favor, tell everyone, you know, your friends, your parents, your girlfriend, oh wait, your dog.
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Tell everyone.
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And especially, please, please tell me any feedback you have, any suggestions i literally read everything i don't always have time to respond
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and i don't always have time to actually implement the suggestions
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but i read every single one i literally obsess over making the site better
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and providing more value to people i'm really looking forward to
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what you guys have to say oh god somebody turned
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that light theme off i don't even know why i implemented
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that but thank you so much for watching hopefully i'll see you pretty soon.
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التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.