cs229-solution
cs229-2019-summer
| cs229-solution | cs229-2019-summer | |
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| 1 | 1 | |
| 2 | 132 | |
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| 4.1 | 0.0 | |
| 4 months ago | over 2 years ago | |
| HTML | HTML | |
| MIT License | - |
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cs229-solution
cs229-2019-summer
-
Are there any good books or videos for beginners?
I would usually recommend starting with Stanford's lectures and when you reach Linear regression you can switch to previous year's. I find 2018 lectures to be much more accessible but 2019 presents some basic concepts in the first lectures that are useful if you don't have the background. Alternatively, there is Caltech's Machine Learning Course.
What are some alternatives?
stanford-CS229 - Python solutions to the problem sets of Stanford's graduate course on Machine Learning, taught by Prof. Andrew Ng [UnavailableForLegalReasons - Repository access blocked]
cs229-2018-autumn - All notes and materials for the CS229: Machine Learning course by Stanford University
stanford-cs229 - 🤖 Exercise answers to the problem sets from the 2017 machine learning course cs229 by Andrew Ng at Stanford
SciMLBook - Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
deep-learning-drizzle - Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
18S096SciML - 18.S096 - Applications of Scientific Machine Learning
ai-deadlines - :alarm_clock: AI conference deadline countdowns