wordlescraper
ML-foundations
wordlescraper | ML-foundations | |
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4 | 1 | |
0 | 3,006 | |
- | - | |
0.0 | 5.4 | |
9 months ago | 26 days ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | MIT License |
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wordlescraper
- Daily Wordle #421 - Sunday, 14 Aug. 2022
- Only lost once (HOMER) but my 99% just turned back into 100% after hitting 200 played. Anyone else?
- 200 up this morning
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Show & Tell - Ever wonder how your wordle score compares to others or why certain words are harder to guess? Check out my project (with Data Science predictive model for a given Word).
I'm open to feedback on any part of this project, including the Data Science part see Jupyter Notebook here.
ML-foundations
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Worried about Calculus
As others have said, you won't need calculus immediately, but it's important that you make a good attempt at learning up to Calc3. I also didn't have a math heavy undergrad so it took a lot of self-study for me, but it's possible. Simulation has a great math boot camp at the beginning to review everything but you'll want to be prepped with Calc before that because that class is all calculus based probability. Some other good resources are the 3Blue1Brown videos on YouTube. They have a great series for both calc & linear algebra to talk through all the intuition with visuals. I also really like John Krohns series because you code through the math which is very applicable for us in this program. I only did his linear Algebra, but he has a whole series with Calc and probability, too. https://github.com/jonkrohn/ML-foundations
What are some alternatives?
facet - Human-explainable AI.
2D-Gaussian-Splatting - A 2D Gaussian Splatting paper for no obvious reasons. Enjoy!
Basic-Mathematics-for-Machine-Learning - The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI
Mathematics-for-Machine-Learning-and-Data-Science-Specialization-Coursera - Mathematics for Machine Learning and Data Science Specialization - Coursera - deeplearning.ai - solutions and notes
cracking-the-data-science-interview - A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
the-elements-of-statistical-learning - My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
ITC - Computer Science coursework and projects at Tec de Monterrey 👨🎓
algorithmica - A computer science textbook
Reinforcement_Learning - RL Algorithms with examples in Python / Pytorch / Unity ML agents
intel-processors - Datasets for All Processors Maufactured By Intel
linear-regression-from-scratch - A data science project for part II physics project E (surveying using stars)