神码ai人工智能写作机器人

机器学习指南 (MACHINE LEARNING GUIDE)

Half of this crazy year is behind us and summer is here. Over the years, we machine learning engineers at Ximilar have gathered a lot of interesting ML/AI material from which we draw. I have chosen the best ones from podcasts to online courses that I recommend to listen to, read, and check. Some of them are introductory, others more advanced. However, all of them are high-quality ones made by the best people in the field and they are worth checking. If you are interested in the current progress of AI or you are just curious about what will be the future then you are on the right page. AI will change all possible fields whether it is physics, law, or retail and one should be prepared for what is to come…

这个疯狂的一年h的ALF已经过去，夏天就在这里。多年来，我们Ximilar的机器学习工程师收集了许多有趣的ML / AI材料，我们从中汲取了经验。我选择了从播客到在线课程中最好的课程，建议您收听，阅读和检查。其中一些是介绍性的，其他则更高级。但是，它们都是由该领域最好的人制造的高质量的，值得一试。如果您对AI的当前进展感兴趣，或者只是对未来会感到好奇，那么您来对地方了。人工智能将改变所有可能的领域，无论是物理学，法律还是零售，都应该为即将发生的事情做好准备……

播客 (Podcasts)

If there is one medium that has become popular in recent years, it must be podcasts. Everyone is doing it right now — there are podcasts about sex, politics, tech, healthcare, brains, bicycles,… and AI is not missing. But one of them stands out. It is a podcast by Lex Fridman. This MIT alumni is doing an incredible job by interviewing top people from the field, famous people included (like Garry Kasparov or Elon Musk). Some episodes are more about science, physics, mind, startups, and the future of humanity. The ideas presented in the podcast are just mind-blowing. The talks are deep but clever and it will take you some time to get through them.

如果有一种媒体近年来变得流行，那么它一定是播客。每个人都在做这件事-关于性，政治，技术，医疗保健，大脑，自行车等的播客，而且人工智能并没有丢失。但是其中之一脱颖而出。这是Lex Fridman的播客。这位麻省理工学院的毕业生通过采访该领域的顶尖人物(包括加里•卡斯帕罗夫(Garry Kasparov)或伊隆•马斯克(Elon Musk))来取得令人称奇的工作。有些情节更多地涉及科学，物理学，思维，创业和人类的未来。播客中提出的想法令人赞叹。会谈虽然很深，但是很巧妙，您将需要一些时间来完成它们。

The Turing test is a recursive test. The Turing test is a test on us. It is a test of whether people are intelligent enough to understand themselves.

图灵测试是一个递归测试。 图灵测试是对我们的测试。 这是对人们是否足够聪明以了解自己的考验。

Another great podcast is Brain Inspired by Paul Middlebrooks with interesting guests. It shows and discusses topics from Neuroscience and AI and how these fields are connected together.

另一个很棒的播客是Paul Middlebrooks带来的Brain灵感与有趣的嘉宾。它显示并讨论了来自Neuroscience和AI的主题，以及这些领域是如何连接在一起的。

图书 (Books)

Life 3.0 by Max Tegmark — How will AI change healthcare, jobs, justice, or war? Max Tegmark is a professor at MIT who has written this provocative and engaging book about the future. He tries to answer a lot of questions like What is intelligence? Can a machine have a consciousness? Can we control AI? … This is a great introduction even for non-technical people.

Max Tegmark撰写的 Life 3.0 —人工智能将如何改变医疗保健，工作，正义或战争？马克斯·泰格马克(Max Tegmark)是麻省理工学院的教授，他写了这本关于未来的极具吸引力的书。他试图回答很多问题，例如什么是智力？机器可以有意识吗？我们可以控制AI吗？ …即使是非技术人员，这也是很棒的介绍。

AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee — Book is about incredible progress in AI in China.

AI超级大国：中国，硅谷和李开复的新世界秩序 -这本书讲述了中国AI的惊人发展。

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron — Do you know how to code and would you like to start with some experiments? This book is not only about one of the most popular programming framework TensorFlow but also about modern techniques in machine learning and neural networks. You will code your first image recognition model and learn how to preprocess and analyze text.

AurélienGéron的Scikit-Learn，Keras和TensorFlow进行动手机器学习 -您知道如何编码，并且想从一些实验开始吗？本书不仅涉及最受欢迎的编程框架TensorFlow之一，而且涉及机器学习和神经网络中的现代技术。您将编写第一个图像识别模型的代码，并学习如何预处理和分析文本。

Deep Learning for Coders with fastai and PyTorch by Jeremy Howard and Sylvain Gugger — Another great book for coders. Code examples are in the PyTorch framework. Jeremy Howard is a famous researcher and developer in the AI community. His fastai project helps millions of people to get into deep learning.

杰西 ·霍华德(Jeremy Howard)和席尔文·古格(Sylvain Gugger) 撰写的Fastai和PyTorch进行的《面向程序员的深度学习》，这是另一本针对程序员的好书。代码示例在PyTorch框架中。杰里米·霍华德(Jeremy Howard)是AI社区的著名研究人员和开发人员。他的fastai项目帮助数以百万计的人进行深度学习。

Looking for more hardcore books with math equations? Then try Deep Learning by MIT Press. Are you interested in classic approaches, then many university students will remember preparing for exams with Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig or the Bishop’s Pattern Recognition and Machine Learning. (These two are a bit advanced and many topics are for a master or even Ph.D. level).

寻找更多具有数学方程式的硬核书籍吗？然后尝试由MIT Press进行深度学习 。您是否对经典方法感兴趣，那么许多大学生会记得准备为人工智能考试做准备： Stuart Russell和Peter Norvig撰写的《 现代方法》或Bishop的模式识别与机器学习。 (这两个有点高级，许多主题是针对硕士甚至博士学位的。)

Magazines

杂志

MIT Technology Review is a great magazine with the latest news and trends in technology and future innovations. The magazine covers also other interesting topics as biotechnology, blockchain, space, climate change, and more. There is a print or digital access option for you.

麻省理工学院技术评论是一本很棒的杂志，其中包含技术和未来创新的最新新闻和趋势。该杂志还涵盖了其他有趣的话题，如生物技术，区块链，空间，气候变化等。有适合您的打印或数字访问选项。

跟随的人 (People to Follow)

There are a lot of famous Scientists & Engineers & Entrepreneurs to follow. For example often mentioned Jeremy Howard (fast.ai), Andrej Karpathy (Tesla AI), Yann LeCun (Facebook AI), Rachel Thomas (fast.ai, data ethics), Francois Chollet (Google), Fei-Fei Li (Stanford), Anima Anandkumar (Nvidia AI), Demis Hassabis (DeepMind), Geoffrey Hinton (Google) and more …

有很多著名的科学家与工程师和企业家。例如经常提到杰里米·霍华德 (fast.ai)，安德烈Karpathy (特斯拉AI)，亚·莱卡 (脸谱AI)，雷切尔·托马斯 (fast.ai，数据伦理)，弗朗索瓦CHOLLET (谷歌)，斐翡丽 (斯坦福大学) ， Anima Anandkumar (Nvidia AI)， Demis Hassabis (DeepMind)， Geoffrey Hinton (Google)等…

讲座和在线课程 (Lectures & Online courses)

So you’ve read some books and articles and now you want to start digging a little deeper? Or you want to become a Machine Learning Specialist? Then start with some online courses. Of course, you will need to learn a little bit about math before and get some basic programming skills. Online courses are a great option if you can’t study at university or you want to get knowledge at your own pace. Here are some of the courses that can serve you as the start point:

因此，您已经阅读了一些书籍和文章，现在想开始更深入地研究吗？或者您想成为机器学习专家？然后从一些在线课程开始。当然，您需要先学习一些数学知识，并掌握一些基本的编程技能。如果您不能在大学学习或希望以自己的速度获取知识，那么在线课程是一个不错的选择。以下是一些可以作为起点的课程：

Machine Learning course from Andrew Ng — this one is a classic and most popular one for a number of reasons, it’s great introductory material.
Ng的机器学习课程-这是一门经典且最受欢迎的课程，由于多种原因，它是很棒的入门资料。
To learn more math we can recommend Mathematics for Machine Learning.
要学习更多数学，我们可以推荐机器学习数学。
Deep Learning specialization is more about modern approaches of neural networks.
深度学习专业化更多地是关于神经网络的现代方法。
There are a lot of great specializations on Udacity by top companies and engineers from various fields like Healthcare or Automotive.
来自Udacity的很多专业公司都来自医疗保健或汽车等各个领域的顶尖公司和工程师。
CS231n and CS224N are great Stanford courses for computer vision and natural language processing (NLP), including video lectures, slides, and materials. It’s FREE!
CS231n和CS224N是斯坦福大学针对计算机视觉和自然语言处理(NLP)开设的一流课程，包括视频讲座，幻灯片和材料。免费！
6.034 and 6.S191 — lectures for AI and Deep Learning by MIT on YouTube.
6.034和6.S191 -MIT在YouTube上进行的AI和深度学习讲座。
Practical Deep Learning for Coders by fast.ai — Jeremy Howard is doing a great job here by explaining concepts, ideas and showing the code in Jupiter notebooks.
fast.ai的《面向程序员的实用深度学习》 —杰里米·霍华德(Jeremy Howard)通过解释概念，想法并在Jupiter笔记本中显示代码，在此方面做得很好。
PyImageSearch — offers great introductory tutorials in the computer vision field
PyImageSearch —在计算机视觉领域提供出色的入门教程

研究博客 (Research blogs)

You know how to code and you even know how to build your CNN? Or are you just simply interested in what is the future of the field and how the companies are using AI? Check out some of the latest trends and SOTA approaches from the top research groups in the world. There are several giants like Facebook, Google pushing the AI boundaries:

您知道如何编码，甚至知道如何构建CNN吗？还是您只是对该领域的未来以及公司如何使用AI感兴趣？查看来自世界顶级研究小组的一些最新趋势和SOTA方法。有几家巨头，例如Facebook，Google突破了AI的界限：

Facebook AI Research — most of the research from the Facebook team is done in Recommender systems, NLP, and Computer Vision.
Facebook AI研究 -Facebook团队的大部分研究都是在Recommender系统，NLP和Computer Vision中进行的。
Google AI Blog — google is probably the most dominant player in AI, check out, for example, their weather prediction system.
Google AI博客 -google可能是AI中最主要的参与者，例如，查看其天气预报系统。
Google Deepmind blog — solving hard problems with AI from healthcare to playing StarCraft 2.
Google Deepmind博客 -解决从医疗保健到玩StarCraft 2的 AI难题。
Open AI Blog — how to solve Rubik’s cube by robotic hand or would you like to generate music on one click?
开放AI博客 -如何通过机械手解决Rubik的多维数据集，或者您想一键生成音乐？
Baidu Research — research blog by one of the largest internet companies in China.
百度研究 -中国最大的互联网公司之一的研究博客。
Malong — research by another Chinese company mostly focused on e-commerce.
Malong-另一家中国公司的研究主要集中于电子商务。
NVIDIA Blog — the biggest GPU creator is doing research in many fields (from accelerating research speed in healthcare to improving the gaming experience).
NVIDIA博客 —最大的GPU创建者正在许多领域进行研究(从加快医疗保健研究速度到改善游戏体验)。
Distill — beautiful and interactive visualizations and explanations of the topics from deep learning, people behind this project are from Open AI, Tesla, Google, …
蒸馏 -深度学习的主题的精美且交互式的可视化和说明，该项目的背后人员来自Open AI，Tesla，Google等。

很棒的文章 (Great articles)

We are always looking for high-quality content that is why some of the following articles can be a bit longer. AI is a complex field which is disrupting the way we live and do business:

我们一直在寻找高质量的内容，这就是以下某些文章可能会更长的原因。人工智能是一个复杂的领域，正在扰乱我们的生活和经商方式：

The New Business of AI article by Andreessen Horowitz.
Andreessen Horowitz撰写 的《 AI的新业务》一文。
The AI Revolution: The road to superintelligence article by Tim Urban.
蒂姆·厄本(Tim Urban)撰写的《人工智能革命：通往超级智能之路》。
The Global AI Index — which country is most innovative and which country is investing the most resources? Right now the USA is still dominating but China is catching up rapidly.
全球AI指数 -哪个国家最具创新性，哪个国家投资最多的资源？目前，美国仍占主导地位，但中国正在Swift追赶。
AI and Efficiency — algorithmic progress has yielded more gains than classical hardware efficiency.
人工智能和效率 -算法的进步比传统的硬件效率产生了更多的收益。
Reflecting on a year of making machine learning actually useful — iterating over dataset is much more important than the latest model architectures.
回顾使机器学习真正有用的一年 -在数据集上进行迭代比最新的模型架构重要得多。

时事通讯 (Newsletters)

Data Science Weekly and Deep Learning Weekly — as the names suggest this is every week news from data science and machine learning.
数据科学周刊和深度学习周刊 -顾名思义，这是每周来自数据科学和机器学习的新闻。
The Algorithm — a newsletter released by MIT.
算法 -麻省理工学院发布的新闻通讯。
The Batch — a newsletter by deeplearning.ai.
The Batch — deeplearning.ai的新闻通讯。
Alignment — a newsletter by Rohin Shah.
对齐 — Rohin Shah的新闻通讯。

趋势与问题 (Trends & Problems)

Ethics & Transparency & Safety — Should countries ban the usage of face recognition technology? [source][source] Is ethical to scrape the data from the internet to build your face search startup? [source] What is an unethical use of AI? [source] What about autonomous weapons for defensive purposes? Are social media polarizing people with their clever algorithms optimized for more clicks/likes/…? [source]
道德与透明度与安全-各国应禁止使用面部识别技术吗？ [ 来源 ] [ 来源 ]从互联网上收集数据来构建您的人脸搜索启动程序是否合乎道德？ [ 来源 ]什么是不道德的AI使用？ [ 来源 ]出于防御目的的自动武器呢？社交媒体是否通过针对更多点击/喜欢/…进行了优化的聪明算法使人们两极分化？ [ 来源 ]
Jobs replacement — Will AI replace all manufacturing and basic jobs? Or will the research in AI create even more job opportunities? What is going to do countries that are heavily dependent on manual work labor? [source] Will one day companies using robots/clever algorithms pay AI Tax?
职位替换-AI是否会替换所有制造业和基本职位？还是人工智能的研究会创造更多的工作机会？严重依赖体力劳动的国家怎么办？ [ 来源 ]有一天，使用机器人/智能算法的公司会缴纳AI税吗？
Interpretability & Explainability — Why did the deep learning model predict X and not Y? What the neural network has actually learned? How can we fool the model with adversarial attacks to make it the wrong prediction?
解释性和Explainability -为什么深度学习模型预测X，而不是Y' 神经网络实际上学到了什么？我们如何通过对抗性攻击来愚弄模型以做出错误的预测？
Racial bias in datasets and models — a big issue mostly in Face recognition, Insurance, and Healthcare. [source]
数据集和模型中的种族偏见-主要在人脸识别，保险和医疗保健方面是一个大问题。 [ 来源 ]
GANs and Deep Fakes — GANs are incredible technology which brings also challenges, … Have you heard about Deep Fakes videos? One day the Deep Fakes will be unrecognizable from genuine content. This could create new problems in politics, business, or our personal lives …
GAN和Deep Fakes-GAN是令人难以置信的技术，它也带来了挑战，…您听说过Deep Fakes视频吗？有一天，从真实内容中将无法识别“深造假”。这可能会在政治，商业或我们的个人生活中产生新的问题……
Big and Small models — bigger models can lead to incredible results in NLP [source]. On the other hand, there is also more research to make models lighter and faster with binarization or pruning techniques.
大，小车型-更大的车型可能会导致不可思议的结果在NLP [ 来源 ]。另一方面，还有更多的研究通过二值化或修剪技术使模型更轻，更快。
Self-supervised learning — high-quality datasets lead to better results, but building such datasets are expensive and requires a lot of manual labeling work. Maybe one day the AI models will be able to create better internal representations without labels.
自我监督的学习-高质量的数据集可以带来更好的结果，但是构建这样的数据集非常昂贵，并且需要大量的手动标记工作。也许有一天，AI模型将能够创建没有标签的更好的内部表示形式。

That is all for now. There are other great resource lists like the one from DeepMind, from which we got inspired. The list is divided by the level of the target audience — introductory, intermediate, and advanced. We will try to keep this post updated and if we find a gem it will appear here. There is much more material from which you can learn but now it’s up to you to start your own machine learning journey.

到此为止。还有其他很棒的资源列表，例如DeepMind的资源列表，我们从中得到了启发。该列表按目标受众的级别划分-入门级，中级和高级。我们将尝试使该帖子保持最新状态，如果我们发现一个宝石，它将显示在此处。您可以从中学习更多的材料，但是现在取决于您自己开始机器学习的旅程。

翻译自: https://medium.com/swlh/the-best-resources-on-artificial-intelligence-and-machine-learning-2231011488bf