stanford-cs229
cs229-2018-autumn
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| stanford-cs229 | cs229-2018-autumn | |
|---|---|---|
| 8 | 112 | |
| 0 | 1,389 | |
| - | - | |
| 0.8 | 2.8 | |
| over 2 years ago | 12 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | - |
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stanford-cs229
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).
cs229-2019-summer - All notes and materials for the CS229: Machine Learning course by Stanford University
cs231n - Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition
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]
coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models
probability - Probabilistic reasoning and statistical analysis in TensorFlow
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
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
TensorFlow-Examples - TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
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