cs229-2019-summer
cs229-2018-autumn
| cs229-2019-summer | cs229-2018-autumn | |
|---|---|---|
| 1 | 112 | |
| 151 | 1,414 | |
| - | - | |
| 0.0 | 3.5 | |
| over 2 years ago | 29 days ago | |
| HTML | Jupyter Notebook | |
| - | - |
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cs229-2019-summer
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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.
cs229-2018-autumn
- cs229-2018-autumn: NEW Courses - star count:949.0
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Mathematics courses for machine learning/deep learning.
Definitely check out CS229: https://cs229.stanford.edu/
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Are there any books I should read to learn machine learning from scratch?
For machine learning (not deep learning), I recommend the lecture notes from Stanford's CS229 course. The reason I really like these notes is because you can find past problem sets that went along with them, and the problem sets are very good: difficult but not impossible, and close to a 50/50 mix of math and programming. I never feel like I've learned a topic just from reading about it, so having good problems to go along with the reading was very important to me.
- cs229-2018-autumn: NEW Courses - star count:834.0
What are some alternatives?
cs229-solution - CS229 Solution (summer 2019, 2020).
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]
SciMLBook - Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
stanford-cs229 - 🤖 Exercise answers to the problem sets from the 2017 machine learning course cs229 by Andrew Ng at Stanford
18S096SciML - 18.S096 - Applications of Scientific Machine Learning
probability - Probabilistic reasoning and statistical analysis in TensorFlow
Machine-Learning-Specialization-Coursera - Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
huggingface_hub - The official Python client for the Huggingface Hub.
Python_Projects
Coursera-Machine-Learning-Stanford - Machine learning-Stanford University
NNfSiX - Neural Networks from Scratch in various programming languages