This article was originally written in Chinese and translated into English by AI.
This guide provides a course introduction, a collection of learning resources, and study advice for Andrew Ng’s Deep Learning Specialization. It is an excellent starting point for beginners in deep learning.
1. Course Introduction
- Course: Deep Learning Specialization
- Instructor: Andrew Ng
- Subjects: neural networks, practical projects, image processing, and natural-language processing
- Prerequisites: high-school derivatives and basic linear algebra
- Difficulty: accessible; recommended for beginners
- Topics: neural networks, practical projects, image processing, and natural-language processing
- Characteristics: accessible explanations that balance mathematical foundations with practical coding
- Intended audience: undergraduate students and above
2. Learning Resources
- Videos: Coursera (paid, with assignments) | NetEase Cloud Classroom (officially licensed, without assignments)
- Code: course materials and assignments (unofficial copy). For the environment required to run the assignments, see Environment Setup.
3. Study Advice
3.1 Course Content
The Deep Learning Specialization is a series of five courses, each requiring approximately two to four weeks of study.
The five courses are:
- 1. Neural-network fundamentals
- 2. Advanced neural-network concepts
- 3. Structuring deep-learning projects
- 4. Convolutional neural networks for images
- 5. Recurrent neural networks for natural language
If you move quickly—watching the videos and completing the coding assignments—you can finish each course in two or three days.
I do not recommend rushing, however. Study the first three courses consecutively at your own pace. Take the fourth and fifth selectively according to your needs: course four for image-related work and course five for natural-language processing.
3.2 What Should I Study for a Quick Introduction?
The first three courses are essential. Then select either image processing, using convolutional neural networks, or natural-language processing, using recurrent neural networks, according to your intended field. You may leave the third course until the end.
The recommended order is therefore 1, 2, 4, 3 for image processing and 1, 2, 5, 3 for natural-language processing.
3.3 What Courses Should I Take Next?
- Computer vision: CS231N
- Natural-language processing: CS224N
- Reinforcement learning: CS294-122
I have completed the entire specialization. If you have questions about its content, feel free to leave a message on the WeChat public account “技术杂学铺” for discussion.