国外AI和深度学习、机器学习和数据科学网站
Towards Data Science: https://towardsdatascience.com/ FQ GOOD
KDnuggets: https://www.kdnuggets.com/
Analytics Vidhya: https://www.analyticsvidhya.com/ FQ
Data Science Central: https://www.datasciencecentral.com/ 一般
Medium: https://medium.com/ FQ
Your adventure to the world of science!
The most progressive, the most cutting-edge, the most exciting… Data science and machine learning are those areas nowadays that are enormously appealing and hot, hot, super-hot topics. But to stay tuned with all the advances and movements in these fields, you need to put lots of effort — researching, reading, checking all the information, news, guides, and other stuff.
This task is far away from being an easy solution. Right now, you can stumble upon a bunch of places with vivid titles and promising headlines, but are they useful enough? Every day I see a crazy flow of information, and, unfortunately, there are lots of false or worthless stuff, and especially on data science and ML. Where to find all the relevant and useful material? — that is the question.
Here is my collection of favorite and trustworthy resources I want to share with you.
Places to make an unforgettable adventure to the world of Machine Learning & Data Science
#1 r/datascience and r/MachineLearning
Both for professionals and amateurs, Reddit is a great place to share information among the scientists and ML-engineers of various experience levels, or just aspiring beginners. You can discuss and debate questions, memes, hot topics, all the latest achievements and more — Reddit will give you a variety of interesting things. Personally I use these sites with sorting filters — I select the hottest and popular topics — very often there are lots of important stuff.
I can’t imagine a data science career without DataCamp. Why so? Of course, there is a perfect option for total beginners and not only. I find that if you are interested in learning a new language or learning a new part of a new language, they are a great way to do that. However, while they are great, they are not enough to make you a data scientist. What I feel is lacking from their program is an actual project where they give you the challenge to solve. They do this to a minimal extent. In my experience, the best way to learn data science is to stumble through some actual projects.
One of the most popular resources on this list. There are probably articles covering all possible directions, questions, and cases — news, jobs, software, events, and more — you can find everything there. So it’s a complete package for data science lovers. You will get information about what’s new happening in data science fields, what courses you need to do, etc. However, KDnuggets is organized a bit differently, and it focuses on industry news, opinions and interviews, publicly available datasets, and data science software.
Datafloq offers information, insights, and opportunities to drive innovation with big data, blockchain, artificial intelligence and other emerging technologies (like data science). The site’s goal is to become a hub for reading high-quality posts, finding big data and technology vendors, connecting with talent, and publishing events. Datafloq offers online training as well. This blog isn’t just for data science practitioners either, also featuring sections on security and the Internet of Things.
It is an online mentoring platform to learn to program, and I am thrilled with it. Its main focus is to provide tutorials for all the amateurs who strive to learn to code, And for ML and data science, this skill is not redundant. The site offers insight from senior developers, customized reading lists, and the ability to connect with developers from all over the world. Hot topics included here are Angular, JavaScript, Node.js, Ruby, and Python. What I like about this site the most is that the people who work there are responsive (given that our time zones are vastly different) — they are professional, and they care about customers and mentors. In my experience, it can be good if you are diligent about screening the mentors. A lot of people will get you into a paid session just to google your errors for you, which isn’t very helpful obviously.
Distill claims to provide clear, dynamic and vivid Machine Learning research. Although it is not so popular among scientists, it really provides great stuff. The vast majority of articles there is interesting research and discoveries — but the most important thing is this — everything is written and edited by top specialists who work in companies like Open AI, Apple, and Tesla.
DATAVERSITY Education is a publisher of educational content for business and Information Technology professionals on the uses and management of data. Their team provides content to its worldwide community of practitioners, experts and developers who benefit from face-to-face hosted conferences, live webinars, white papers, online training daily news and articles, and blogs. They also offer a free weekly newsletter.
Data Science Central is perhaps the best independent data science blog on the web. Designed for big data practitioners, the site provides a community experience that includes an expansive editorial platform, social interaction, forum-based technical support, and the latest in technology, tools, and trends, along with a classifieds section for industry job opportunities. Data Science Central also offers webinars and a unique membership package that provides access to everything on the site for free.
First, what it doesn’t do: It doesn’t introduce you to Machine Learning. It won’t walk you through what Neural Nets are, the math behind word embeddings, and all that. You’ll have to pick up the theory elsewhere. It won’t take you from zero to hero. You need a foundation of math and command of programming before you can tackle machine learning.
But when you’ve brushed up on Matrices, have some sort of idea what a ‘Tensor’ is, when you have learned about various AI approaches from Support Vector Machines to Convolutional Neural Nets, and are ready to experiment and to build, MachineLearningMastery provides a working, simple example of every goddamn thing you could possibly imagine.
Data Science Dojo offers five-day public and private data science bootcamps. It features a community of mentors, students, and professionals committed to the field, and more than 3,600 users from 700 countries have graduated from the program. The Dojo blog provides a wide range of content spanning data science basics all the way up to more advanced topics like ethics and security and access control.
This is an exciting company that is going DataRobot transforms and accelerates predictive analytics with automated machine learning. What is great is that this company not only do great stuff but also provides the latest updates on what’s happening in the world of automated machine learning and data science.
Nate Silver’s data science blog, FiveThirtyEight, is one of the best data science blogs for analyzing the latest and greatest in the world of data science. The blog’s articles routinely feature interactive examples and in-depth articles detailing the ways in which data applies to politics, culture, the economy, and other facets of everyday life.
Data Science 101 provides all the resources aspiring data scientists will need while learning the tricks of the trade. Run by Ryan Swanstrom, the blog provides a constant stream of content, with topics ranging from the top companies to work for if you are a data scientist to job interview tips. Data Science 101 includes an active user community as well, and there are even an open Facebook group readers can join if they want to continue the conversation.
TDS strikes a good balance between solid machine learning and practical examples. There is a wealth of quality articles written by practicing Data Scientists. I see TDS as a place where Data Scientists and other machine learning practitioners document what they are working on, which is exactly what a good blog should be. TDS is able to promote praxis while not shying away from theory when it’s needed. While there is a slight oversupply of Deep Learning, TDS seems much less beguiled by DL than other sources, which is great for real-world Data Scientists actively trying to solve data-driven challenges.
insideBIGDATA is a news outlet that offers news, strategies, products, and services in the world of big data for data scientists and IT and business professionals. Their editorial focuses on big data, data science, AI, machine learning and deep learning. Its team of content producers features some of the brightest minds in the field, and really caters to the technical industry professionals looking to keep tabs on the most cutting-edge facets of machine learning and AI.
It is a big software company and the company has an amazing blog that features a number of articles and guides on all sorts of software like Hadoop, Apache, etc. which is very useful.
It is a research laboratory based in San Francisco, California. They offer comprehensive resources on AI — a blog, research papers, and interesting articles. Everything is up-to-date provided by the experts in their field.
#18 Tombone’s Computer Vision Blog
Deep Learning, Computer Vision, and the algorithms that are shaping the future of Artificial Intelligence.
This iss a free weekly newsletter with top data science picks from around the web. Covering machine learning, data visualization, analytics, and strategy. Definitely worth subscribing!
I can’t imagine a life without StackOverflow, can you? Stack Overflow — it’s an open community for people who spend their lives coding and are looking for answers to all types of questions or simply enjoy searching through interesting threads. It’s a great platform for sharing your knowledge and discovering new things.
Machine learning has been one of the most prominent buzzwords in recent years. Some people say the term in hushed tones, the fear of a robot takeover filling their minds, while others are positively giddy at the prospect. The reality is both more mundane and more promising than either of these two groups would assume.
Often used interchangeably with the term “artificial intelligence”, machine learning is all about using predictive algorithms to make life easier. Think about fridges that tell you when you’re out of food, Roombas that know where to clean, and self-driving cars that actually minimize the chance of a collision. It’s a huge improvement in the quality of life, and that is always in demand. Many companies are already using machine learning to their advantage, so staying on top of the latest trends is the best way to ensure that your business is future-proof.
We realize that the inner workings of machine learning may be complex and intimidating if you just dive deep into them without preparation, which is why we’ve compiled this list of blogs and resources. Since Python is very often the soil from which machine learning can grow, you can say we know a thing or two about the topic. Read on to learn where to start your machine learning adventure and how to stay up to date.
Blogs
1. KDnuggets
One of the longest-running and most popular blogs on the topic of machine learning to this day. The fact that it’s still so widely frequented despite the fact that its user interface looks like it was there when the wheel was invented just goes to show that function really trumps form in the long run. Updated regularly with rich, informative content, KDnuggets has guides for beginners as well as intermediate users, alongside information on various courses and events that can bring you up to speed on all things machine learning.
Notable post: The Beginner’s Guide to Data Integration Approaches in Business Intelligence is a great example of how KDnuggets’ writers explain specific machine learning-related concepts alongside their practical implementations.
2. Algorithmia
https://algorithmia.com/bloghttps://algorithmia.com/blogEDX
If you’re looking for a blog that will give you a good rundown of everything related to machine learning, Algorithmia is a good starting point. These Seattle-based experts have quite vast reserves of knowledge that they happily share in digestible blog posts. They will help you learn about the most basic aspects of artificial intelligence as well as more advanced concepts.
Notable post: Machine learning examples is a very thorough post that does a good job of introducing the concept of machine learning to an entry-level reader. As the title suggests, it lists real-life examples of practical machine learning implementations.
3. Medium
https://medium.com/topic/machine-learningMachine Learning Crash Course
Medium hosts a large number of informative blogs, and its machine learning section is particularly helpful. What you’ll notice right away is the light, familiar tone, as exemplified by articles such as How I Accidentally Created an Infinite Pixel Hellscape, but don’t let that immediate impression fool you into thinking that’s all there is to it. With frequently published articles written both by amateur programmers and industry experts, you’ll find a healthy dose of tips based on real, practical knowledge, providing answers to the most pressing questions you may have on all things machine learning.
Notable post: The Story of How AI changed Google Maps is an extensive take on the influence machine learning has had on the popular app. It’s a fascinating read, showcasing exactly how the technology in question can make a big impact.
4. Towards Data Science
https://towardsdatascience.com/Pandas
Hosted by Medium, Towards Data Science features plenty of high-quality articles written by contributors from all round the world. A dedicated team of international experts works on reviewing the content written by the community to ensure that every single piece that gets published is worth your time. You’ll find entire sections dedicated to machine learning and AI, each of them filled to the brim with highly educational content.
Notable post: This tutorial on how to build an app that generates photorealistic faces is a great example of Towards Data Science’s approach. The article is practical, digestible, and well-researched.
5. Distill
Calling this entry a blog is an understatement. Distill an online research journal that provides info on some of the latest developments in machine learning. What this entails is that the content here will be a bit more advanced, certainly beyond entry-level, but it is nonetheless presented in an accessible way. Of particular note are the graphics, with advanced yet clear reactive diagrams perfectly illustrating the sometimes confusing processes described in the articles.
Notable post: One of the best recent posts that drive the point home that the graphics make all the difference is one that deals with the visualization of neural networks. The various graphs and charts found throughout do a good job of making this complicated matter much easier to understand.
6. DeepMind’s Blog
https://distill.pub/Elements of AI
The DeepMind Blog
If you’re looking for experts on the topic, it’s best to look for those who deal with it on a daily basis. DeepMind is a British AI company, currently owned by Google. Their posts are mostly reports and think pieces related to their current research, so by following the blog closely you might learn a few things no one else is talking about yet. If you’re tired of reading and would prefer to listen to something instead, they also have podcasts.
Notable post: Thanks to DeepMind’s blog you get to see exactly how AI can change the world through its practical implementation. Their post on WaveNet technology and how it can help speech-impaired people restore their old voices gives you a glimpse into the potential of machine learning to improve lives.
7. The TensorFlow Blog
https://www.tensorflow.org/Quora
If you’ve already gotten your feet wet and are ready for deeper waters, Tensorflow is the place to go. The blog section is incredibly technical, definitely not for absolute neophytes, but even if you’re more familiar with the topic and are looking for more specific knowledge that will help you hone your machine learning skills, this is the blog for you. It also offers courses with certificates to boost your business’s credibility in the field.
Notable post: An article on WebAssembly backend for TensorFlow.js not only explains the concept itself but also provides detailed instructions on how you can use the WASM backend to your advantage.
8. PyImageSearch
https://www.pyimagesearch.com/blog/
Libraries are an important element of machine learning, but how exactly do you use them? PyImageSearch offers a lot of case studies and practical examples that will help you learn all about libraries at a pace you’re comfortable with. The tutorials on the blog can get pretty detailed and they are some of the best sources of information about libraries.
Notable post: A good example of these tutorials is the guide on OpenCV’s “dnn” module with NVIDIA GPUs, CUDA and cuDNN. Reading through this piece you can see how specific these tutorials can get. The blog post itself goes into very deep detail on each step of the procedure, so that by the end of it you know exactly how to deal with the problem in question.
9. O’Reilly
https://www.oreilly.com/radar/topics/ai-ml/
With O’Reilly’s blog, it’s easy to stay on top of the industry trends as their posts often come from leading influencers. It will help you understand how artificial intelligence and machine learning are implemented by businesses, giving you the inspiration and tools you need to make sure your business remains relevant.
Notable post: A perfect example is their article on 5 key areas for tech leaders to watch in 2020. It provides a clear picture of all the current trends and how they should influence your operations.
Other resources
1. Kaggle
Perhaps machine learning sounds like a great tool for you, but you’re not exactly willing to spend time learning it from the ground up. You want to take advantage of it as a company, however, so you’re still going to need some experts to handle it for you. Luckily, Kaggle is here to help you out with just that. Many experts who started out by providing their services through Kaggle have gone on to set up their own companies in the field, making this site a veritable breeding ground for talent. You can get involved by organizing competitions, ensuring that the code and datasets you get are created with the greatest amount of precision.
2. EDX
EDXhas amassed an impeccable selection of courses for all machine learning enthusiasts, regardless of whether they’re just starting out or have advanced knowledge. EDX can help you join courses hosted by some of the most prestigious institutions in the world and point you in the direction of the most reputable resources.
3. Machine Learning Crash Course
https://developers.google.com/machine-learning/crash-course/ml-intro
What better place to learn all about machine learning than at the source. Google’s crash course is exactly what it says on the tin, but that doesn’t even begin to cover the sheer utility of this resource. It’s very neatly divided into units that are composed of several guides in video or text form, as well as practical activities. They come with a time estimation to give you flexibility in terms of deciding when and how you study.
4. Pandas
If you’re looking for libraries to get started with your machine learning endeavors, Pandas is definitely worth a look. Based on Python, Pandas began development in 2008. Since then, it’s been a reliable open-source library boasting many useful features, such as fast and efficient DataFrame object data manipulation, greatly optimized performance, and incredible flexibility. It’s perfect for both academic and commercial purposes.
5. Practical Machine Learning with Python
https://www.youtube.com/watch?list=PLQVvvaa0QuDfKTOs3Keq_kaG2P55YRn5v&v=OGxgnH8y2NM
Hosted by sentdex, this series of YouTube videos provides a comprehensive overview of machine learning and its practical applications. The course requires knowledge of Python, but you’ll learn everything else you need to know along the way. It’s the perfect guide that’s both digestible and exhaustive if you’re looking for something in a video format.
6. Elements of AI
https://course.elementsofai.com/
Elements of AI
A crash course on AI that took Finland by storm is now available in a number of languages for free. With a pleasant design and short quizzes at the end of every course to test how much you’ve learned, this may just be one of the most accessible and effective courses you’ll take.
7. Quora
Quora has info on pretty much anything, often provided directly by experts in the field. AI is no exception in that regard: you’ll find a range of topics that will help you boost your grasp of the technology. With questions on machine learning, artificial intelligence, deep learning and more, all answered in one place, you have a handy source of instant knowledge.
8. Reddit
Much like Quora, Reddit pretty much has a subreddit for everything. While they’re not as extensive as Quora’s topics, Reddit is a community that will help you figure things out on your own, as well as provide bespoke guides for very obscure things. You can start with r/MachineLearning, r/artificial, and r/learnmachinelearning, to name just a few.
Final thoughts
It’s no secret that machine learning is the future, and gaining an understanding of it might determine whether your business will succeed.
Thankfully, the topic is endlessly fascinating, and knowledge that could potentially be world-changing is available to you in digestible forms. Even if you’re already familiar with machine learning, the rapid pace at which this field is expanding means that there’s always something new and exciting to learn.
Treat our list as your starting point, regardless of whether you’re at the beginning of your machine learning journey or you’re after more advanced resources.
Top 20 Websites for Machine Learning and Data Science in 2020 by Benthecoder
https://medium.com/swlh/top-20-websites-for-machine-learning-and-data-science-d0b113130068
下面是我列出的获取有价值资源和新闻的最佳机器学习和数据科学网站。
数据科学在世界上几乎所有地方都呈指数级增长。 数据科学家是非常受欢迎的，因为他们似乎有“神奇的”能力，可以从数据中为数据驱动的公司和组织创造价值。
今天，因为互联网，任何人都可以成为数据科学家。
是的，你没听错。 你不需要一个计算机科学的大学学位或者深度学习的博士学位就可以胜任数据科学家的工作。
只要你能做公司希望你做的事——从数据中提供有价值的见解，帮助他们发展业务和做出决策ーー你就是一名数据科学家。
因此，成为其中之一的关键在于这个等式:
激情+知识+组合+运气+关系=数据科学领域的工作
在本文中，我将帮助你获得数据科学的大量必备知识。
数据科学的流行仍处于早期阶段，现在进入该领域正合适。 如果你是一个初学者，正在寻找合适的免费资源来学习数据科学，或者只是想了解新的数据科学新闻，那么你来对地方了。 知道这个领域正在迅速变化是至关重要的，更新是必要的，这样你就不会在大数据的浪潮中落后。
我已经为你做了繁重的工作，并编辑整理了我认为是数据科学和机器学习的最佳的20个网站，这是一个数据科学博客，研究人员和新闻网站的汇编，我每天阅读，以了解该领域的最新进展。 这些频道涵盖了广泛的主题，从观点到最佳实践，包括该领域的一些顶尖人才。
这份名单排名不分先后，它们都有自己的优点。 所以现在向下滚动阅读列表吧！
我用来评论网站的模板如下
简介:
适合水平: 初学者 / 中级 / 专家 / 所有
类型: 博客/教程/视频/观点/新闻/研究论文等
功能:
最大优点:
最佳文章: 文章链接
网站链接
1. Machine Learning Mastery
简介: 这个网站的所有者Jason Brownlee拥有人工智能硕士和博士学位，他曾经为国防、创业公司和天气预报研究机器学习系统。
适合水平: 所有
类型: 博客，教程
功能: 教程用一种自顶向下的方法，专注于端到端的通过一个数据集，在流行的平台如 Scikit-learn，R 和 Keras 上做实验得到一个结果。
最大优点: 这个网站对于初学者来说非常友好，因为它帮助你直接编程来做机器学习，而不是阅读大量的概念和理论。
最佳文章: 当我第一次开始学习机器学习时，他的入门指南对我非常有帮助，因为它让我了解了什么是机器学习，以及如何开始。
https://machinelearningmastery.com/
2. Elite data science
简介: 时间有限的专业人士和初学者倾向于寻找一条数据科学的捷径，只学习最相关的，商业上可行的工具和最佳实践，Elite Data Science正是为他们准备的。 它跳过了无关紧要的理论和数学，而是带领你通过最直接的路径专业的应用 DS 和 ML。
适合水平: 所有
类型: 指南，概念解释，代码教程，职业帮助，工具和资源
功能: 如上所述，这个网站在解释概念，学习代码，获取职业上的帮助，和找DS和ML的工具和资源方面非常好用。
最大优点: EDS 的专题数据科学入门是一个微型课程，提供了一个数据科学和应用机器学习的简介。 根据他们的说法，如果你是一个开发人员、分析师、经理或者有抱负的数据科学家，想要学习更多关于数据科学的知识，来这里就对了。
最佳文章: 2019年如何成为数据科学家
3. KDnuggets
简介: KDnuggets 是人工智能、分析学、大数据、数据挖掘、数据科学和机器学习领域的顶级网站，由 Gregory Piatetsky-Shapiro 和 Matthew Mayo 编辑。
适合水平: 所有
类型: 新闻 / 博客，意见，教程，工作，公司，课程，数据集
功能: KDnuggets 是一个很棒的网站，里面有很多有用的资源，你可以每天查看它的最新新闻，也可以通过教程开始学习。
最大优点: 它有一个过去30天的热门故事的页面，展示了前7名最受欢迎和分享最多的文章。
最佳文章: 未来的数据科学与分析职业
4. Kaggle
简介: 由Anthony Goldbloom and Ben Hamner创立，Kaggle 以竞赛和数据集而闻名。 而新功能——notebooks也是任何人开始数据科学的完美平台。
适合水平: 所有
类型: 竞赛，数据集，Notebooks，讨论，课程
功能: Kaggle
是学习和参加比赛的最佳选择。 它更像是一个学习平台，在这里你可以运用你的技能和天赋，与其他数据爱好者竞争。 你也可以在 Kaggle
使用它自己的笔记本来编写代码，它提供了一个无需设置、可定制的 Jupyter Notebooks 环境，它提供了免费的 gpu
和社区发布的数据和代码的巨大存储库。
最大优点: Kaggle Learn 和竞争是机器学习和数据科学中最好的平台之一。 问问任何一个数据爱好者，他们都会知道 Kaggle 是什么。 笔记本电脑也是伟大的，因为它消除了需要安装任何软件，你可以跳入编写代码轻松。
最佳文章: Kaggle 博客
5. Reddit — r/datascience
简介: r / 数据科学是一个讨论数据、分享数据科学经验、成为数据科学家的建议、数据处理等等的地方！
适合水平: 所有
类型: 所有
功能: 一个提问、共享资源、交流等等的平台。
最大优点: 这个平台特别棒，因为在这个领域工作的专家和数据科学家可以谈论他们的经验，并与初学者和 DS 爱好者分享他们的工作生活。 偶尔社区也会分享学习和掌握一项技能的优质资源。
最佳文章:
https://www.reddit.com/r/datascience/
6. Towards Data Science
介绍: TDS 团队是一个由15人组成的国际团队，负责在 Towards Data Science 上发布内容。 他们每天都会审阅来自世界各地的作者的作品，并向众多作者提供反馈意见。 他们的目标是呈现读者热衷阅读的写得好，内容丰富的文章。
适合水平: 所有
类型: 数据科学，机器学习，编程，可视化，人工智能
功能: TDS 是一个阅读和学习平台。 如果你是 Medium 的作者，那么你可以向他们的专栏提交文章，如果你幸运的话，你将有机会为 TDS 写作。 当我乘坐火车上下班时，我会阅读 TDS，上面的文章总是一流的，干货十足。
最大优点:
最佳文章：
https://towardsdatascience.com
7. Analytics Vidhya
简介: Analytics Vidhya 由 Team AV 管理，为 Analytics 和数据科学专业人员提供基于社区的知识门户。 他们的目标是成为一个完整的门户，服务所有数据科学专业人员的知识和职业需求。
适合水平: 所有
类型: 博客课程，Hackathons，Bootcamp (付费)
功能: AV 适合那些寻找一个平台来参加Hackathons比赛和练习他们的 DS 技能的人。 博客和文章也是信息丰富和有趣。
最大优点: AV 有学习路径为各种工具、技术和竞赛的端到端过程提供明确的方向。 AV自己的 DS 适应性测试DSAT对于那些希望测试他们的技能和每天练习的人来说是完美的。 有些比赛是免费的，而且经常举行。
最佳文章: 6个有挑战性的开源数据科学项目，让你成为更好的数据科学家
http://www.analyticsvidhya.com
8. Data Science Dojo
简介:
DSD 由Raja Iqbal创立，最初是西雅图的一个meetup group。 为了让每个人都能接触到数据科学，Raja
离开了当时在微软的工作，开始在世界各地的数据科学训练营(DSDaaB)教授数据科学。 简而言之，数据科学 Dojo
的目标就是教会每个人如何使用数据来发现新的见解并做出明智的决定。
适合水平:所有
类型: 博客，视频教程
功能: 这个网站是为那些寻找数据科学，数据工程，大数据分析，人工智能和机器学习的教程和文章的人准备的。
最大优点: 网站非常直观和易用。 特色文章明确突出显示在网站的顶部，向下滚动你会看到顶部标签，如 # datascience 和 # analytics 等。
最佳文章: 101个数据科学面试问题，答案和关键概念
blog.datasciencedojo.com
9. Data Science 101
简介: 这个博客的创建者 Ryan Swanstrom 是大数据领域的思想领袖，被视为互联网上数据科学领域最具影响力的人物之一。
适合水平: 所有
类型: 博客，新闻
功能: 这是一个纯粹的博客，用于阅读有关概念或数据科学的最新消息。
最大优点: 网站简洁明了。 也没有广告，这意味着你可以安静愉快地阅读文章。
最佳文章: 如果你是一个数据科学家，最值得为之工作的公司
https://101.datascience.community/blog/
10. Geeks for Geeks — Machine Learning
简介: 极客是另一个牛逼的计算机科学极客博客。 这个网站上几乎什么都有，从算法和数据结构到面试和实习。 他们的机器学习部分是现象级的，为初学者提供了一条学习的路径。
适合水平: 所有
类型: 博客，教程，文章
功能: 这是一个大规模的文章集合，分类良好，还是初学者寻找免费资源的一个好地方。
最大优点: 它有实践问题帮助人们掌握机器学习和 DS 所需的编程。
最佳文章: 开始机器学习
www.geeksforgeeks.org
11. Google News — Data Science
简介: 谷歌新闻——数据科学报道数据科学领域的最新消息和最新动态。 从教程和博客到行业新闻和创业公司。 你可以阅读无数来自福布斯，TechRepublic，Infoworld 等的文章。
适合水平: 所有
类型: 新闻
功能: 非常适合每天阅读。
最大优点: 谷歌新闻有很棒的用户界面，还有 Android 和 iOS app。 所以你可以在任何地方阅读它。
最佳文章: 数据科学家是做什么的以及如何与他们一起工作
news.google.com
12. Datafloq
简介: Datafloq 是大数据，区块链和人工智能的一站式源。 它们提供信息、洞察力和机会来推动新兴技术的创新。
适合水平: 所有
类型: 博客
功能: 阅读文章以了解最新的人工智能技术和大数据新闻非常有趣。
最大优点: 内容结构组织得非常好。
最佳文章: 物联网和机器学习如何让我们的道路更安全
datafloq.com
13. Domino Data Science Blog
简介: Domino Data Lab的使命是支持数据科学家实现他们的工作和潜力的期望。 在以下页面和频道中可以详细了解他们如何帮助加速数据科学家的工作，生产: 数据科学, 代码, 机器学习, 实用技术, 工作领导者, 模型管理 & 工程
适合水平: 中级
类型: 博客
功能: 这个博客有为中级数据科学家准备的很好的文章，他们涉及的东西是相当先进的。
最大优点: 该网站设计的很好且易于浏览。 文章分门别类，条理分明。
最佳文章: 纽约时报的数据科学
blog.dominodatalab.com
14. data36
简介: 本博客的作者Tomi Mester是一位数据分析师和研究员。 在他的博客中，你可以抢先一睹在线数据分析师最佳实践。 你可以找到关于数据分析、 AB测试、研究、数据科学等方面的文章和视频。
适合水平: 中级
类型: 博客
功能: 可以阅读关于数据科学的文章和跟踪最新的关于 DS 工具的教程。
最大优点: 一个50分钟的视频课程如何成为一个数据科学家。
最佳文章: 学习数据科学ー4条不为人知的真理
data36.com
15. Revolutions
简介: Revolution是关于人工智能、机器学习和数据科学的新闻综述。 它有一个有趣的合集，包含博客文章，软件公告和来自微软和其它地方的数据应用程序，由网站所有者Revolution Analytics最近精选。
适合水平: 中级
类型: 新闻
功能: 这个博客只是一个最新消息的渠道，所以你可以阅读关于开源人工智能，机器学习和数据科学，以及每月更新的行业新闻。 这个博客也致力于为 R 社区成员提供他们感兴趣的新闻和信息。 所以你最好是 R 用户。
最大优点: 网站干净简洁，易于浏览。
最佳文章: 数据科学新闻
blog.revolutionanalytics.com
16. Edwin Chen
简介:
Edwin Chen ー 在MIT 学的数学 / 计算机科学 / 语言学，在MSR 做语音识别，在Clarium 做量化交易，在Twitter
做广告，在Dropbox 做数据科学，在Google 做统计 / 机器学习。
他经营着这个博客，并且很擅长用自己的方式解释人工智能、统计学、数据科学和人类计算。
适合水平: 专家
类型: 博客
功能: 复习你的人工智能，统计学，数据科学和机器学习知识。
最大优点: 漂亮的网站和清晰的代码展示。
最佳阅读: 赢得Netflix奖：总结
blog.echen.me
17. Pete Warden’s Blog
简介: Pete Warden 是 Jetpac 公司的CTO，O’Reilly的书《The Public Data Handbook》和《The Big Data Glossary》的作者，OpenHeatMap 和 Data Science Toolkit等开源项目开发者。 他的博客有令人惊叹的结构精良的高质量短文章。
适合的水平: 专家
类型: 博客
功能: 这个博客适合想深入挖掘一切数据和发现的热衷数据科学的读者。
最大的优势: 博客干净利落，读起来不错。
最佳文章: 机器学习需要什么样的硬件
petewarden.com
18. InsideBIGDATA
简介: insidebligdata 是一家新闻机构，为数据科学家以及 IT 和商业专业人士提供大数据领域的新闻、战略、产品和服务。
适合水平: 中级，专家
类型: 博客
功能: 用于阅读和追踪最新的新闻。
最大优点: 特别章节包含有关数据科学、开发人员、用例、访谈等文章。
最佳文章: 利用机器学习的力量扩展小型数据团队
https://insidebigdata.com/
19. Google AI Blog
简介: Google AI正在进行推动该领域的最新技术的研究，将AI应用于产品和新领域，并开发工具以确保每个人都可以访问AI。 他们的博客带来了Google AI的最新消息。
适合水平: 专家
类型: 研究论文
功能: Google AI blog是让AI专家跟上Google AI的所有新进展的地方。
最大优点: 关于AI最新进展的内容丰富的论文。
最佳文章: 可教机器2.0让人工智能更容易
ai.googleblog.com
20. Nature Machine Intelligence
简介：Nature是一本国际性的周刊，出版所有科学技术领域最好的同行评议研究。
Nature Machine
Intelligence对人工智能、机器学习和机器人领域的最佳研究感兴趣，并提供有关上述领域的快速、权威、深刻和引人注目的新闻。
所有的编辑决定都是由一个全职的专业编辑团队做出的。
适合水平: 专家
类型: 观点，新闻，研究论文
功能: 阅读机器智能的最新消息和进展。
最大优点: Nature拥有顶尖研究人员和专家撰写的高质量文章。 他们的消息来源可靠，值得信赖。
最佳文章: 带机器人购物
www.nature.com
以下是这些网站的列表:
- Machine Learning Mastery
- Elite data science
- KDnuggets
- Kaggle
- Reddit — r/datascience
- Towards Data Science
- Analytics Vidhya
- Data Science Dojo
- Data Science 101
- Geeks for Geeks — Machine Learning
- Google News — Data Science
- Datafloq
- Domino Data Science Blog
- data36
- Revolutions
- Edwin Chen
- Pete Warden’s Blog
- InsideBIGDATA
- Google AI Blog
- Nature
国外最好的人工智能媒体和技术博客
https://blog.csdn.net/weixin_42793005/article/details/81209364
国外AI和深度学习博客
https://blog.csdn.net/lei_chen/article/details/103966903
https://zhuanlan.zhihu.com/p/97523127
https://blog.csdn.net/tkkzc3E6s4Ou4/article/details/81784726
https://www.kaggle.com/general/307498
https://www.linkedin.com/pulse/top-20-machine-learning-data-science-websites-follow-2020-kharkovyna
https://www.stxnext.com/blog/best-machine-learning-blogs-resources/
