这些是在线上最好的免费人工智能教育资源

by Mariya Yao

姚iya(Mariya Yao)

这些是在线上最好的免费人工智能教育资源 (These are the best free Artificial Intelligence educational resources online)

Deep learning is not a beginner-friendly subject — even for experienced software engineers and data scientists. If you’ve been Googling this subject, you may have been confused by the resources you’ve come across.

深度学习不是一门适合初学者的课程，即使对于经验丰富的软件工程师和数据科学家而言也是如此。 如果您一直在搜索该主题，则可能会对所遇到的资源感到困惑。

To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended.

为了找到最佳资源，我们对工程师进行了调查，选择了他们最喜欢的深度学习资源，这就是他们的建议。

These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

这些教育资源包括在线课程，面对面的课程，书籍和视频。 所有这些都是完全免费的，并且由著名教授，研究人员和行业专家(例如Geoffrey Hinton，Yoshua Bengio和Sebastian Thrun)设计。

机器学习 (Machine Learning)

Deep learning techniques build off of and often combine with classic machine learning methodologies. If you don’t know the difference between supervised and unsupervised learning, or think “gradient descent” is some kind of Photoshop tool, you should definitely take one of the courses below first to get caught up.

深度学习技术基于经典的机器学习方法，并且经常与之结合。 如果您不知道有监督学习与无监督学习之间的区别，或者认为“梯度下降”是某种Photoshop工具，那么您绝对应该首先学习以下课程之一。

1)斯坦福大学的吴安德(Andrew Ng)的机器学习(在线课程) (1) Andrew Ng’s Machine Learning at Stanford University (online course))

Before Andrew Ng became Chief Scientist at Baidu, he taught machine learning at Stanford and co-founded Coursera, the world’s first MOOC (massively open online course) platform. Ng’s gentle introduction to machine learning course is perfect for engineers who want a foundational overview of key concepts in the field.

在吴安德成为百度首席科学家之前，他在斯坦福大学教授机器学习，并与他人共同创立了Coursera，这是世界上第一个MOOC(大规模开放在线课程)平台。 Ng 对机器学习课程的详尽介绍非常适合希望对该领域的关键概念有基本概述的工程师。

To supplement the online course, you’ll want to check out the lecture notes, problem sets, and Matlab code samples under Ng’s formal Stanford’s CS 229 — Machine Learning course offered at the university.

为了补充在线课程，您需要查看Ng在大学提供的正式Stanford's CS 229 —机器学习课程下的讲义，习题集和Matlab代码示例。

2)Sebastian Thrun机器学习入门(在线课程) (2) Sebastian Thrun’s Introduction To Machine Learning (online course))

Sebastian Thrun has a long history of innovating in A.I. and autonomous vehicle technology, first winning the DARPA Grand Challenge with Stanford’s Stanley team in 2005. He also directed Stanford’s artificial intelligence laboratory, started Google’s self-driving car division, and founded Udacity, another MOOC platform with excellent offerings in machine learning and artificial intelligence.

塞巴斯蒂安·特伦(Sebastian Thrun)在人工智能和自动驾驶技术创新方面拥有悠久的历史，2005年与斯坦福大学的斯坦利团队一起赢得了DARPA大挑战。他还领导斯坦福大学的人工智能实验室，成立了Google的自动驾驶汽车部门，并成立了另一个MOOC Udacity。平台，在机器学习和人工智能方面提供出色的产品。

Thrun’s “Introduction To Machine Learning” course is a robust introduction to the subject and also the foundation for a Data Analyst “nanodegree” certification sponsored by Facebook and MongoDB.

Thrun的“ 机器学习入门 ”课程是对该主题的详尽介绍，也是Facebook和MongoDB赞助的数据分析师“纳米学位”认证的基础。

Also offered on Udacity is Thrun’s “Introduction to Artificial Intelligence” which teaches the fundamentals of A.I. as well as applications such as robotics, computer vision, and natural language processing. This course leads into the Machine Learning Engineer nanodegree sponsored by Kaggle.

在Udacity上还提供了Thrun的“ 人工智能简介 ”，其中讲授了AI的基础知识以及诸如机器人技术，计算机视觉和自然语言处理之类的应用程序。 本课程通向由Kaggle赞助的机器学习工程师纳米学位。

深度学习 (Deep Learning)

Even though neural networks were invented in the 1960’s, deep learning only became viable and popular in recent years due to the explosion of big data and computational power. Once you’ve covered the basics of machine learning, you can start learning about this exciting new field in artificial intelligence.

尽管神经网络是在1960年代发明的，但是由于大数据和计算能力的爆炸式增长，深度学习直到最近才变得可行和流行。 掌握了机器学习的基础知识之后，您就可以开始学习人工智能这个令人兴奋的新领域。

1)杰弗里·欣顿的机器学习神经网络(在线课程) (1) Geoffrey Hinton’s Neural Networks For Machine Learning (online course))

Widely credited as the “father of deep learning,” Geoffrey Hinton is a University of Toronto professor and Google Researcher. Hinton’s UT lab put “deep learning” into mainstream media in 2012 with their surprising win of a Merck drug discovery challenge despite no one on the team having any molecular biology expertise. Suddenly, the New York Times started featuring headlines like “Scientists See Promise In Deep Learning Programs.”

杰弗里·欣顿(Geoffrey Hinton)被广泛认为是“深度学习之父”，是多伦多大学的教授兼Google研究员。 欣顿大学的UT实验室在2012年将“深度学习”带入了主流媒体，尽管他们的团队中没有人拥有任何分子生物学专业知识，但他们却意外地赢得了默克公司的药物发现挑战 。 突然，《纽约时报》开始刊登标题为“ 科学家看到深度学习计划的承诺 ”的标题。

Alums of Hinton’s lab have continued his legacy. Yann LeCun, formerly a postdoctorate research associate in Hinton’s lab, is a leading innovator in convolutional neural nets and now directs Facebook’s AI Research. Ilya Sutskever went on to co-found and act as Research Director of OpenAI (backed by Elon Musk). Brendan Frey, inspired by a personal tragedy, went on to found Deep Genomics, a startup that applies deep learning to genomic medicine and therapy.

欣顿实验室的校友继承了他的遗产。 Yann LeCun是​​Hinton实验室的前博士后研究助理，是卷积神经网络的领先创新者，现在负责Facebook的AI研究。 Ilya Sutskever继续共同创立OpenAI(由Elon Musk支持)并担任研究总监。 受个人悲剧启发，Brendan Frey创立了Deep Genomics，这是一家将深度学习应用于基因组医学和疗法的创业公司。

Taking Hinton’s “Neural Networks For Machine Learning” course on Coursera won’t automatically turn you into a brilliant artificial intelligence pioneer, but the class is certainly a helpful start.

在Coursera上学习 Hinton的“ 用于机器学习的神经网络 ”课程不会自动将您变成出色的人工智能先驱，但无疑是一个有益的起点。

2)杰里米·霍华德(Jeremy Howard)的Fast.ai和数据学院证书(在线和面对面课程) (2) Jeremy Howard’s Fast.ai & Data Institute Certificates (online and in-person courses))

Jeremy Howard was President & Chief Scientist of Kaggle before founding Enlitic, a company that applies deep learning to medical diagnoses and clinical decisions, and Fast.ai, an educational resource for deep learning engineers.

杰里米·霍华德(Jeremy Howard)曾是Kaggle的总裁兼首席科学家，之后成立了将深度学习应用于医学诊断和临床决策的公司Enlitic和深度学习工程师的教育资源Fast.ai。

He also teaches in-person deep learning courses along with researcher Rachel Thomas at the University of San Francisco’s Data Institute. Deep Learning Part One covers the basics of Deep Learning, while Part Two covers advanced applications. The in-person certificate courses are not free, but all of the content is available on Fast.ai as MOOCs.

他还与旧金山大学数据研究所的研究员Rachel Thomas一起教授现场深度学习课程。 深度学习第一部分介绍了深度学习的基础知识，而第二部分介绍了高级应用程序。 面对面的证书课程不是免费的，但是所有内容都可以在MOC上的Fast.ai上获得。

Howard and his teaching team work hard to curate diverse students because they’ve observed that the A.I. industry is severely lacking in women, people of color, LGBTQ, and other minority representation. Potential students who fall within these underrepresented groups are encouraged to apply for diversity fellowships in order to attend.

霍华德(Howard)和他的教学团队努力工作，以培养多元化的学生，因为他们发现AI行业严重缺乏女性，有色人种，LGBTQ和其他少数族裔代表。 鼓励属于这些代表性不足群体中的潜在学生申请多样性奖学金 ，以便参加。

3)Yoshua Bengio和Ian Goodfellow的深度学习(本书) (3) Deep Learning by Yoshua Bengio & Ian Goodfellow (book))

Yoshua Bengio, professor at University of Montreal, is another leading figure driving forward the deep learning industry. His papers have been cited over 40,000 times on Google Scholar. His former student, Ian Goodfellow, is now a researcher at OpenAI and best known for inventing Generative Adversarial Networks.

蒙特利尔大学教授Yoshua Bengio是推动深度学习产业发展的另一位领导人物。 他的论文在Google学术搜索中被引用了40,000多次。 他的前学生伊恩·古德费洛(Ian Goodfellow)现在是OpenAI的研究员，并以发明创造对抗网络而闻名。

Their book, Deep Learning, published by MIT Press, is freely available online and conveniently includes applied math refreshers on Linear Algebra, Probability Theory, and Numeric Computation prior to diving into core deep learning concepts.

他们的书《 深度学习 》由麻省理工学院出版社出版，可在网上免费获得，并且在深入学习核心深度学习概念之前，还可以方便地在线性代数 ， 概率论和数值计算方面应用数学进修。

4)Michael Nielsen撰写的《神经网络与深度学习》(本书) (4) Neural Networks & Deep Learning by Michael Nielsen (book))

Michael Nielsen’s ever evolving book on “Neural Networks & Deep Learning” was recommended over and over again. Nielsen, a Research Fellow at YCombinator Research, prefers to explain core principles in intuitive and memorable ways rather than drown you in “a hazy understanding of a long laundry list of ideas.”

一遍又一遍地推荐了迈克尔·尼尔森(Michael Nielsen)不断发展的有关“ 神经网络和深度学习 ”的书。 YCombinator Research的研究员Nielsen宁愿以直观且令人难忘的方式解释核心原理，而不是让您陷入“对冗长的想法清单的朦胧理解”中。

Nielsen’s book focuses on teaching you how to solve a concrete problem — teaching a computer to recognize handwritten digits — with neural networks. You start with a simple neural network and gradually improve upon your code as new concepts are introduced.

Nielsen的书着重于教您如何使用神经网络解决具体问题(教计算机识别手写数字)。 您从一个简单的神经网络开始，随着新概念的引入逐步改进您的代码。

If you don’t have the strongest grasp of the prerequisite mathematics for deep learning or are not an experienced programmer, Nielsen’s book is especially beginner-friendly. The code for the course exercises are written in Python 2.7 and relatively easy to understand even if you don’t normally use the language.

如果您对深度学习的先决条件数学没有最深刻的了解，或者您不是经验丰富的程序员，那么Nielsen的书特别适合初学者。 该课程练习的代码是用Python 2.7编写的，即使您通常不使用该语言，也相对容易理解。

5)使用TensorFlow进行深度学习(在线课程) (5) Deep Learning With TensorFlow (online course))

Once you’ve mastered the conceptual groundwork of deep learning and neural networks using any of the previous resources, you’ll want to master the tools to turn theory into practice. While numerous deep learning frameworks and libraries exist, TensorFlow by Google has quickly become one of the most popular and best supported.

使用任何先前的资源掌握了深度学习和神经网络的概念基础之后，您将需要掌握将理论付诸实践的工具。 尽管存在大量的深度学习框架和库，但Google的TensorFlow已Swift成为最受欢迎和得到最好支持的平台之一。

Udacity’s Deep Learning by Google online course is taught by Vincent Vanhoucke, a Principal Scientist at Google, and technical lead in the Google Brain team. The course assumes intermediate to advanced grasp of machine and deep learning concepts and extends your knowledge to training logistical classifiers, simple deep networks, and convolutional and recurrent neural networks with TensorFlow.

Udacity的Google深度学习在线课程由Google首席科学家，Google Brain团队的技术负责人Vincent Vanhoucke教授。 该课程假定您对机器和深度学习概念有中级到高级的掌握，并将您的知识扩展到使用TensorFlow训练后勤分类器，简单的深度网络以及卷积和递归神经网络。

TensorFlow’s website also offers beginner and advanced tutorials and strong community support. Videos from their most recent developer summit are available here and describe a slew of new features.

TensorFlow的网站还提供初学者和高级教程以及强大的社区支持 。 最新开发者摘要的视频可在此处获得，并介绍了许多新功能。

6)牛津大学深层自然语言课程(视频和讲义) (6) Oxford Deep NLP Course (VIDEOS & LECTURES))

For those of you with an interest in natural language processing and understanding, Oxford recently published course videos and lectures from their “Deep Natural Language Processing” course taught by DeepMind experts like Phil Blunsom and Chris Dyer. This advanced and applied course covers NLP topics like analysing latent dimensions in text, speech-to-text transcription, machine translation, and Q&A systems.

对于那些对自然语言处理和理解感兴趣的人，牛津大学最近发布了他们的“ 深度自然语言处理 ”课程的课程视频和讲座，这些课程和课程由DeepMind专家Phil Philnsom和Chris Dyer教授。 这门高级的应用课程涵盖了NLP主题，例如分析文本中的潜在维度，语音转文本转录，机器翻译和问答系统。

7)NIPS会议视频存档(视频) (7) NIPS Conference Video Archive (video))

Advanced practitioners of deep learning flock to the increasingly more popular NIPS (Neural Information Processing Systems) conference every year to hear the top researchers present their breakthrough papers and discoveries.

深度学习的高级从业人员每年涌入越来越受欢迎的NIPS(神经信息处理系统)会议，以听取顶级研究人员介绍他们的突破性论文和发现。

If you’ve missed the conference in the past or simply can’t make the event in person, check out the NIPS video archives from 2015 and 2016.

如果您过去错过了会议，或者根本无法亲自参加会议，请查看2015和2016年的NIPS视频档案。

8)科学论文 (8) Scientific Papers)

New papers are published every day in the artificial intelligence and deep learning space. Google Scholar, ArXiv, and Research Gate are great repositories to start with, but many more collections exist.

每天都会在人工智能和深度学习领域发表新论文。 Google Scholar ， ArXiv和Research Gate是很好的存储库，但是还有更多的集合。

If you’re wondering which papers to start with, here is a starting list of foundational research papers to read. Once you start reading papers, Andrej Karpathy created a useful tool aptly named ArXiv Sanity which will recommend related work.

如果您想知道从哪些论文开始，这里是基础研究论文的起始列表 。 一旦开始阅读论文，Andrej Karpathy就会创建一个有用的工具，名为ArXiv Sanity ，它将推荐相关工作。

To be alerted to new papers, you can subscribe to the RSS feeds of these two ArVix sections: computer learning and machine learning. The most popular articles also tend to bubble up on Reddit Machine Learning or Hacker News.

要获得新文章的提醒，您可以订阅以下两个ArVix部分的RSS feed： 计算机学习和机器学习 。 最受欢迎的文章还倾向于在Reddit机器学习或Hacker News上冒出来。

If you own an Amazon Echo and want to geek out hands-free, you can use ArXivML, an Alexa Skill that will read recent abstracts for you.

如果您拥有Amazon Echo并想免提使用，则可以使用ArXivML ，这是一种Alexa技能，可以为您阅读最新的摘要。

结论 (Conclusion)

With the best minds in artificial intelligence freely offering a wide range of educational resources, everyone interested in deep learning should be able to find content that suits their learning style and level.

借助人工智能领域的顶尖人才，他们可以自由地提供广泛的教育资源，因此对深度学习感兴趣的每个人都应该能够找到适合自己学习风格和水平的内容。

Beginners can start with Andrew Ng’s online course and Michael Nielsen’s accessible book, while advanced engineers can dive right into Geoffrey Hinton’s classic Neural Networks course, start learning Tensorflow, and stay updated with the latest scientific research.

初学者可以从Ng的在线课程和Michael Nielsen的无障碍书籍开始 ，而高级工程师可以直接进入Geoffrey Hinton的经典神经网络课程，开始学习Tensorflow并保持最新的科学研究最新信息。

Did we miss any deep learning education resources from industry leaders? Please let us know in the comments below. And check out our blog for more articles like this.

我们是否错过了行业领导者提供的任何深度学习教育资源？ 请在下面的评论中告诉我们。 并查看我们的博客以获取更多类似这样的文章。

翻译自: https://www.freecodecamp.org/news/how-to-get-the-best-artificial-intelligence-education-for-free-21af8c47e36b/