内容简介:TheThis is the best place to start, as it is not overwhelming, is self-contained, and is not focused on any particular sub-topic.Yeah, this place is incredible. Make sure you check AI and Data Science as topics of interest and start following people and pu
#1 The Batch
The deeplearning.ai weekly newsletter . Each week, it packs recent break-throughs and noteworthy news alongside a short intro by Andrew Ng himself. The best thing about The Batch is that it is quick, self-contained, and always includes some perspective on the news, such as “why it matters.”
This is the best place to start, as it is not overwhelming, is self-contained, and is not focused on any particular sub-topic.
#2 Medium
Yeah, this place is incredible. Make sure you check AI and Data Science as topics of interest and start following people and publications that have similar interests as you. Most of what I see on The Batch I also see here but in more detail. Sometimes I find stuff here before it even gets to The Batch.
The distinctive feature of Medium is the voices. You often see people that work at major companies, startup CEOs, or Ph.D. students of top universities. The tech stuff is more often than not being written by people that are actually in tech. They are not written by trained journalists but by real damn nerds. The cherry goes to the comment section. You are not only reading from these folks, but you also get the chance to talk to them directly and ask anything.
#3 Twitter
Find the people whose work you like and follow them on Twitter. This is enough for the platform to email you their most important tweets. I was surprised by the number of useful links I started receiving after I started doing this. If you don’t know where to start, begin by following the guests of #5.
Twitter is where the news is born.After I created my account and started following AI people, I began to see stories that would only pop up on larger channels after weeks. Before anyone can write a piece on something, its author has already twitted about it. I don’t use twitter for anything else.
#4 YouTube
Similar to Medium, YouTube has many tech people doing essays on AI. The bad thing about YouTube is that it doesn’t have as much curation nor a helpful email digest. The positive side is that it is less demanding on you. Watching a video is way more relaxing to the eyes than reading articles.
My recommendations are TwoMinutesPapers and Yannic Kilcher . The former focuses on presenting eye-pleasing research on AI and graphics, and the latter is more in-deep on analyzing AI papers down to the math. I confess that I don’t really like Yannic’s videos that much, as they are too long, but I love his selection. Whenever he uploads something, I search for the paper to read.
#5 AI Podcast
Lex Fridman is a former MIT professor that runs a podcast on AI and Computer Science. The selection of guests is outstanding. It includes Turing Award winners, Nobel winners, and many brilliant individuals, such as 3Blue1Brown’s host Grant Sanderson and Elon Musk . To me, the beauty of this podcast is that Lex always asks his guests to explain fundamental concepts, but the conversation never shies away from going technical.
This is the place to get perspective.Where else can you find Noam Chomsky and Daniel Kahneman discussing AI’s future? Or maybe the lead engineer behind TensorFlow discussing its history in-depth and not being shy of talking technical? Reading papers is nice, but hearing what the people behind some of the most ground-breaking research to date have to say helps a lot with understanding what is happening now and what will likely happen next.
以上就是本文的全部内容,希望本文的内容对大家的学习或者工作能带来一定的帮助,也希望大家多多支持 码农网
本站部分资源来源于网络,本站转载出于传递更多信息之目的,版权归原作者或者来源机构所有,如转载稿涉及版权问题,请联系我们。
数据结构与算法分析(C++版)(第3版)
Clifford A. Shaffer / 张铭、刘晓丹、等译 / 电子工业出版社 / 2013 / 59.00元
本书采用当前流行的面向对象的C++程序设计语言来描述数据结构和算法, 因为C++语言是程序员最广泛使用的语言。因此, 程序员可以把本书中的许多算法直接应用于将来的实际项目中。尽管数据结构和算法在设计本质上还是很底层的东西, 并不像大型软件工程项目开发那样, 对面向对象方法具有直接的依赖性, 因此有人会认为并不需要采用高层次的面向对象技术来描述底层算法。 但是采用C++语言能更好地体现抽象数据类型的......一起来看看 《数据结构与算法分析(C++版)(第3版)》 这本书的介绍吧!