跟读练习: The BEST Coding Interview Roadmap in 2023 (free) - 通过视频学习英语口语
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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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本课的词汇与口语要点
这节 C1 级别的口语课以视频“The BEST Coding Interview Roadmap in 2023”为素材。 视频中反复出现的词有：graph, tree, learn, programming, dynamic。 这段视频共有 118 个句子、1686 个单词可供跟读。 讲话部分时长为 8:20。 说话人语速较快，每分钟约 202 个词，会出现较多连读和弱读。 只有 81% 的单词属于英语最常用的 3,000 词，词汇难度较高。
视频中的重点词汇
视频中最难的 15 个单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| graph 名词 | /ɡɹæf/ | 圖表 /图表 |
| pointer 名词 | /ˈpɔɪn.təɹ/ | 教鞭 |
| algorithm 名词 | /ˈælɡəɹɪðm̩/ | 算法, 演算法 |
| binary 形容词 | /ˈbaɪ.nə.ɹi/ | 二進 /二进 |
| dimensional 形容词 | /daɪˈmɛn.ʃə.nəl/ | 維的 /维的 |
| interval 名词 | /ˈɪntɚvəl/ | 間隔 /间隔, 間隙 /间隙 |
| hash 名词 | /ˈhæʃ/ | 井號 /井号, 井字 |
| greedy 形容词 | /ˈɡɹiːdi/ | 貪婪的 /贪婪的, 貪心的 /贪心的 |
| manipulation 名词 | /məˌnɪp.juˈleɪ.ʃən/ | 操縱 /操纵, 運用 /运用 |
| leak 名词 | /liːk/ | 漏洞 |
| visualization 名词 | /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/ | 可視化 /可视化, 視覺化 /视觉化 |
| node 名词 | /noʊd/ | 結節 /结节 |
| concise 形容词 | /kənˈsaɪs/ | 簡明 /简明 |
| obsess 动词 | /əbˈsɛs/ | 迷住, 著魔 |
| opinionated 形容词 | /əˈpɪn.jə.neɪ.tɪd/ | 有主見的 /有主见的 |
视频中出现的短语动词
| 单词 | 释义 |
|---|---|
| check out 动词 | 退房 |
| end up 动词 | 到达 |
| figure out 动词 | 弄清楚 |
| go through 动词 | 通過 /通过 |
| look forward to 动词 | 期待, 向往 |
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 现在完成进行时 have/has been + -ing — 以前开始、现在仍在继续的动作 | I've been wanting · have been asking |
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | being introduced · could be said · be related |
| 定语从句 who / which + 从句 — 补充说明人或事物 | heaps, which are · graphs, which are · sorting, which is |
需要注意的发音
说话人用了 32 次缩略和弱读形式，例如 don't, you're, I'm。请按听到的简短形式来说。
- “th” 音: algorithm /ˈælɡəɹɪðm̩/, thumbnail /ˈθʌm.neɪl/
- “sh” 和 “zh” 音: dimensional /daɪˈmɛn.ʃə.nəl/, specialization [ˌspɛʃəlaɪ̯ˈzeɪ̯ʃn̩], hash /ˈhæʃ/, manipulation /məˌnɪp.juˈleɪ.ʃən/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/
- 长单词——注意重音位置: dimensional /daɪˈmɛn.ʃə.nəl/, specialization [ˌspɛʃəlaɪ̯ˈzeɪ̯ʃn̩], manipulation /məˌnɪp.juˈleɪ.ʃən/, visualization /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/, opinionated /əˈpɪn.jə.neɪ.tɪd/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 graph, pointer, algorithm 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。