跟讀練習: The BEST Coding Interview Roadmap in 2023 (free) - 通過影片學習英語口語
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118 句
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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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本課的詞彙與口語要點
這節 C1 級別的口語課以影片“The BEST Coding Interview Roadmap in 2023”為素材。 影片中反覆出現的詞有：graph, tree, learn, programming, dynamic。 這段影片共有 118 個句子、1686 個單詞可供跟讀。 講話部分時長為 8:20。 說話人語速較快，每分鐘約 202 個詞，會出現較多連讀和弱讀。 只有 81% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。
影片中的重點詞彙
影片中最難的 15 個單詞，附發音和釋義：
| 單詞 | 發音 | 釋義 |
|---|---|---|
| graph 名詞 | /ɡɹæf/ | 圖表 /圖表 |
| pointer 名詞 | /ˈpɔɪn.təɹ/ | 教鞭 |
| algorithm 名詞 | /ˈælɡəɹɪðm̩/ | 演算法, 演算法 |
| binary 形容詞 | /ˈbaɪ.nə.ɹi/ | 二進 /二進 |
| dimensional 形容詞 | /daɪˈmɛn.ʃə.nəl/ | 維的 /維的 |
| interval 名詞 | /ˈɪntɚvəl/ | 間隔 /間隔, 間隙 /間隙 |
| hash 名詞 | /ˈhæʃ/ | 井號 /井號, 井字 |
| greedy 形容詞 | /ˈɡɹiːdi/ | 貪婪的 /貪婪的, 貪心的 /貪心的 |
| manipulation 名詞 | /məˌnɪp.juˈleɪ.ʃən/ | 操縱 /操縱, 運用 /運用 |
| leak 名詞 | /liːk/ | 漏洞 |
| visualization 名詞 | /ˌvɪʒ.ʊ.ə.laɪˈzeɪ.ʃən/ | 視覺化 /視覺化, 視覺化 /視覺化 |
| node 名詞 | /noʊd/ | 結節 /結節 |
| concise 形容詞 | /kənˈsaɪs/ | 簡明 /簡明 |
| obsess 動詞 | /əbˈsɛs/ | 迷住, 著魔 |
| opinionated 形容詞 | /əˈpɪn.jə.neɪ.tɪd/ | 有主見的 /有主見的 |
影片中出現的短語動詞
| 單詞 | 釋義 |
|---|---|
| check out 動詞 | 退房 |
| end up 動詞 | 到達 |
| figure out 動詞 | 弄清楚 |
| go through 動詞 | 通過 /通過 |
| look forward to 動詞 | 期待, 嚮往 |
影片中的語法
說話人最常用的結構，並附上影片中的原話：
| 結構 | 影片中的用法 |
|---|---|
| 現在完成進行時 have/has been + -ing — 以前開始、現在仍在繼續的動作 | I've been wanting · have been asking |
| 定語從句 who / which + 從句 — 補充說明人或事物 | heaps, which are · graphs, which are · sorting, which is |
| 被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的 | being introduced · could be said |
需要注意的發音
說話人用了 32 次縮略和弱讀形式，例如 don't, you're, I'm。請按聽到的簡短形式來說。
- “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/
- 長單詞——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/
如何用這段影片練習
- 先完整聽一遍影片，不要開口，記下不認識的單詞。
- 先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
- 錄下自己的聲音並與原聲對比，特別注意 graph, pointer, algorithm 這類單詞。
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。