＃1 Fastai面向程序員的實用深度學習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發布了fastai庫及其深度學習課程的新版本！

• Course page

  課程頁面

• YouTube Playlist

  YouTube播放列表

• Fastai forum

  法泰論壇

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!

對于任何想開始實踐性深度學習的人， 這是我的第一建議 。 從第一天開始，Fastai自上而下的教學方法就可以開始將您的知識應用于實際問題，并逐步深入研究細節。 此外，您將學習使用fastai庫，它是一個非常強大的工具，可以提高所有深度學習項目的工作效率！

＃2 deeplearning.ai深度學習專業 (#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在線課程不是免費的。 但是，所有課程視頻都可以在YouTube上免費獲得 ！

• Course 1: Neural Networks and Deep Learning

  課程1： 神經網絡與深度學習

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

  課程2： 改善深度神經網絡：超參數調整，正則化和優化

• Course 3: Structuring Machine Learning Projects

  課程3： 構建機器學習項目

• Course 4: Convolutional Neural Networks

  課程4： 卷積神經網絡

• Course 5: Sequence Models

  課程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，您可以從這些與深度學習頂尖研究人員的訪談中獲得更多啟發： 深度學習英雄

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.

這些課程為您提供了深度學習的一個很好的概述，涵蓋了一些重要的主題，并且有深入的介紹，但由Andrew Ng進行了簡單說明。 我建議您在完成fastai課程后觀看這些播放列表。

＃3 MIT深度學習課程 (#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.

麻省理工學院的課程是另一個很棒的資源！ 如果您想進一步深入研究在線深度學習，請考慮以下播放列表。

• MIT 6.S191: Introduction to Deep Learning

  MIT 6.S191：深度學習簡介

• Deep Learning State of the Art (2020)

  深度學習最先進的技術(2020)

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

如果您觀看了前兩門課程，則這些課程可能會為您提供免費的信息，并為您提供一些以前未涵蓋的主題的更多觀點。

接下來是什么？ (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.

學習任何技能都需要實踐和很多時間的工作。 可用的免費在線資源足以讓您成為頂尖的深度學習從業人員。 有時最困難的部分是保持動力。 觀看課程不會使您成為專家，而是將您的知識付諸實踐。 為此， Kaggle可能是最好的地方，您可以在其中練習實際問題，并與成百上千的其他競爭對手分享想法。 當您參加第一場Kaggle比賽時，您可能會很難取得好的成績。 不放棄就是讓您學到最多，獲得直覺并成為該領域專家的原因。

總結和進一步閱讀 (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!

如果您覺得這個故事有用，請考慮通過此鏈接加入我的郵件列表 這樣您就不會錯過任何我即將發表的故事！ 您可以在以下故事中閱讀有關我的深度學習之旅的更多信息！

Thanks for reading! Have a great day!

謝謝閱讀！ 祝你有美好的一天！