＃1 Fastai面向程序员的实用深度学习2020 (#1 Fastai Practical Deep Learning for Coders 2020)

On 21 of August 2020, fastai released the new version of the fastai library and of their Deep Learning course!

2020年8月21日，fastai发布了fastai库及其深度学习课程的新版本！

• Course page

  课程页面

• YouTube Playlist

  YouTube播放列表

• Fastai forum

  法泰论坛

This is my #1 recommendation for anyone wanting to start with practical Deep Learning. Fastai top-down teaching approach allows you to start applying your knowledge on real-world problems since day 1 and gradually dive into the details. Furthermore, you will learn to use the fastai library that is a very powerful tool to increase your productivity in all your Deep Learning projects!

对于任何想开始实践性深度学习的人， 这是我的第一建议 。 从第一天开始，Fastai自上而下的教学方法就可以开始将您的知识应用于实际问题，并逐步深入研究细节。 此外，您将学习使用fastai库，它是一个非常强大的工具，可以提高所有深度学习项目的工作效率！

＃2 deeplearning.ai深度学习专业 (#2 deeplearning.ai Deep Learning Specialization)

The deeplearning.ai online courses on Coursera are not free. However, all the course videos are freely available on YouTube!

Coursera上的deeplearning.ai在线课程不是免费的。 但是，所有课程视频都可以在YouTube上免费获得 ！

• Course 1: Neural Networks and Deep Learning

  课程1： 神经网络与深度学习

• Course 2: Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

  课程2： 改善深度神经网络：超参数调整，正则化和优化

• Course 3: Structuring Machine Learning Projects

  课程3： 构建机器学习项目

• Course 4: Convolutional Neural Networks

  课程4： 卷积神经网络

• Course 5: Sequence Models

  课程5： 序列模型

Additionally, you can get further inspiration from these interviews with top Deep Learning researchers: Heroes of Deep Learning

此外，您可以从这些与深度学习顶尖研究人员的访谈中获得更多启发： 深度学习英雄

These courses give you a good overview of Deep Learning, covering important topics with some depth but explained in a simple way by Andrew Ng. I would recommend watching these playlists after completing fastai course.

这些课程为您提供了深度学习的一个很好的概述，涵盖了一些重要的主题，并且有深入的介绍，但由Andrew Ng进行了简单说明。 我建议您在完成fastai课程后观看这些播放列表。

＃3 MIT深度学习课程 (#3 MIT Deep Learning Courses)

MIT courses are another great resource! If you want to go even deeper into your Deep Learning studies online, consider the following playlists.

麻省理工学院的课程是另一个很棒的资源！ 如果您想进一步深入研究在线深度学习，请考虑以下播放列表。

• MIT 6.S191: Introduction to Deep Learning

  MIT 6.S191：深度学习简介

• Deep Learning State of the Art (2020)

  深度学习最先进的技术(2020)

If you watched the previous two courses these ones may give you complimentary information and additional perspective on some topics not covered before.

如果您观看了前两门课程，则这些课程可能会为您提供免费的信息，并为您提供一些以前未涵盖的主题的更多观点。

接下来是什么？ (What next?)

Learning any skill requires practice and many hours of work. The free online resources available are more than enough for you to become a top deep learning practitioner. Sometimes the hardest part is to keep the motivation on. Watching the courses won’t make you an expert, putting your knowledge in practice will. For that Kaggle is probably the best place to be, where you can practice in real-world problems and share ideas with hundreds or thousands of other competitors. When you join your first Kaggle competition you will probably struggle to get a good result. Not giving up is what will make you learn the most, gain intuition and become an expert in the field.

学习任何技能都需要实践和很多时间的工作。 可用的免费在线资源足以让您成为顶尖的深度学习从业人员。 有时最困难的部分是保持动力。 观看课程不会使您成为专家，而是将您的知识付诸实践。 为此， Kaggle可能是最好的地方，您可以在其中练习实际问题，并与成百上千的其他竞争对手分享想法。 当您参加第一场Kaggle比赛时，您可能会很难取得好的成绩。 不放弃就是让您学到最多，获得直觉并成为该领域专家的原因。

总结和进一步阅读 (Final remarks and Further Reading)

If you find this story useful please consider joining my mailing list in this link so that you won’t miss any of my upcoming stories! You can read more about my Deep Learning journey on the following stories!

如果您觉得这个故事有用，请考虑通过此链接加入我的邮件列表 这样您就不会错过任何我即将发表的故事！ 您可以在以下故事中阅读有关我的深度学习之旅的更多信息！

Thanks for reading! Have a great day!

谢谢阅读！ 祝你有美好的一天！