
大多数人为编程面试做准备时,会刷数百道LeetCode题目,希望碰到原题。但面试官一问变体,他们就懵了。 本课程则采取相反方法。不学150种解法,而是掌握真正产生这些解法的约20种模式——滑动窗口、双指针、单调栈、回溯、图遍历、动态规划等。每节课教授一个思维模型:何时使用、精确模板以及如何调整。最终,你将能看透任何陌生问题,解读其线索,并知道匹配哪种模式。 通过44节课,你将覆盖Blind 75、NeetCode 150和Grokking模式集中的所有问题——按照精心设计的依赖顺序,从复杂度基础到主"决策树"和实时面试执行。示例语言无关,然后翻译成你的目标语言并针对你的目标公司。 你不需要是天才或记住所有东西。你需要一个系统。报名并构建一个能让下一个变体感觉熟悉的系统。
For Roger, the interesting part of artificial intelligence begins after the experiment succeeds. His work has involved turning research prototypes into dependable software: packaging language and vision models behind production APIs, building distributed data and feature pipelines, automating training and deployment, and monitoring accuracy, latency, drift, and infrastructure cost once systems are live. He has contributed to recommendation engines, document-understanding tools, forecasting services, and generative-AI applications using Python, C++, PyTorch, cloud platforms, and containerized infrastructure. Equally comfortable profiling an inference bottleneck, reviewing model behavior with data scientists, or explaining tradeoffs to a product team, Julian specializes in closing the distance between a promising model and a product people can actually rely on.
Good structure to follow along to get a big picture of the coding interview. But don't expect that you can become an "expert" after finishing the course. You still need to practice hundreds of hours on leetcode - but the big picture really help you find the pattern in your practice
This course covers a wide range of aspects in DSA, without going into any depth. Some people might say this is not deep enough to be an expert, but honestly this is all we need in the time when AI does the real deep work
This course is mostly AI generated, but that's exactly what I need - not bounded by one guy's knowledge limit
Gracias por preparar el curso en español
You can probably get away in coding interview by throwing around a lot of the terminologies you learn in this crash course