Study route for learners in machine learning / deep learning / computer vision
This learning schedule is sorted out for reseachers or whoever interested in machine learning / deep learning / computer vision. It's also most welcomed for instructors to refer to this schedule to train beginner students. Each course listed below takes about two weeks to finish if you are fully dedicated to it.
The skills you need to develop a machine learning / deep learning / computer vision project include:
These courses are for beginners. It's suggested to follow the order below. Try to finish the coding and mathematics homework in each course. Everyone should have covered the following staffs before your REAL journey to machine learning. It does not mean you have to take the following courses, we recommend you to do so; but if you are not totally fresh, you should know the contents.
Quick intro to machine learning, no math but a very good overview about what ML is and what ML does. We recommend approx. 2 weeks for study. The MATLAB assignments are not longer recommended.
Quick intro to deep learning, with emphasis on computer vision. You could do some interesting staffs after taking this course (and code practice!), e.g., face recognition, pedestrian detection and segmentation, medical image diagnosis, image generation.
After finishing the above courses, it's highly suggested to join some simple competitions before you keep going on Kaggle
, a well-known data science competition website. You can refer to others' code for inspiration. Free GPU resources are also available on Kaggle. For beginners, the Getting Started
category is the best place to obtain project experience and practice coding skills. The following two competitions are good basic options.
Digit Recognizer
A classification task based on hand-written digit images. A convolutional neural network might be involved. For this competition, we provide some reference code
with different mahcine learning computing package.Titanic: Machine Learning from Disaster
A classification task based on structured data.We recommend ML freshman should know the following packages:
Now we need to expand our sight to the current research topics in machine learning / deep learning / computer vision.
Hsuan-Tien Lin (NTU)'s Machine Learning Foundations Homepage
video link: Youtube
Bilibili
Hsuan-Tien Lin (NTU)'s Machine Learning Techniques Homepage
video link: Youtube
Bilibili
Hung-yi Lee (NTU)'s Deep Learning Homepage
video link: Youtube
Bilibili
By far, you should be familiar with the basic concepts of machine learning / deep learning / computer vision. You might need to participate in a real project in a lab at school (choose a reputed lab carefully) or in a IT company. You may also consider join a more advanced competition on Kaggle
.
Here, we provide a PyTorch coding template
in python for developing a real project.
Don't rush to dig into these advanced courses. These courses are more specific for certain topics. Only after you have several project experiences, can these advanced courses help you build up a systematic sense of these topics.
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Bilibili
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YouTube
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At this point, you have mastered the basic skill and knowledge required for machine learning / deep learning / computer vision research. But there are still so much unknown placed waiting for you to explore. What you learn here merely provides you with the way leading to those places. Begin you adventure now! And enjoy the beauty of maching learning! Ads
Here is a rough instruction in Chinese
) (Here is the packages list required to set up a system for machine learning research
)Any advice or comments to improve this learning schedule is most welcomed.
Jiancheng Yang
who provides the primary study route and first start this project.Linguo Li
who provides the MNIST reference code and packages list.