cs229-2018-autumn
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cs229-2018-autumn | Python_Projects | |
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112 | 18 | |
1,389 | 17 | |
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2.8 | 6.1 | |
14 days ago | 10 months ago | |
Jupyter Notebook | Python | |
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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
Python_Projects
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Is it normal to not be able to deeply comprehend at this stage?
Howdy, sorry that you are struggling with the machine learning concepts. As an additional resource you may be able to benefit from, here is my DNN from scratch : https://github.com/JTexpo/Python_Projects/blob/main/Neural_Networks/DNN_Math_Selenium/PythonBot/deep_neural_network.py
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What do you guys actually automate using python?
For ethical reason I won't provide the exact solution; however, a simplification can be found on my GitHub: https://github.com/JTexpo/Python_Projects/tree/main/Neural_Networks/DNN_Math_Selenium
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GitHub Repo Feedback [intermediate / advance]
Howdy, lately I have been wanting to educate other on machine learning and and AI. I have placed my code here: https://github.com/JTexpo/Python_Projects/tree/main , and was hoping for some feedback on how I could make the code / repo even more user friendly or just overall remove any obvious code smells.
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Howdy! Work in Progress, GNN to play Jetpack Joyride. Tutorial coming soon
GitHub: https://github.com/JTexpo/Python_Projects (more ML projects!) YouTube: https://www.youtube.com/@jtexpo (Tutorials over GitHub projects)
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Machine learning A-Z (grad level course/resources)
I've also been recently trying to showcase how AI works, and have been building models from scratch using different concepts ranging from neural networks, to minimax, decision trees, and even graph theory if you are interested. My solutions can be found on my GitHub and YT Personal GitHub: https://github.com/JTexpo/Python_Projects YT (covers GitHub code): https://www.youtube.com/@JTexpo
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what is more accurate way to define variable?
Howdy, I would strongly recommend using as much as possible when defining stuff in code. It may be a hot take to some; however, there is nothing worse than re-reading (or worse refactoring) code with poor naming conventions and no docstring. If you are interested in how I name my variables, my most recent GitHub push was this: https://github.com/JTexpo/Python_Projects/tree/main/SPD_Battleship where I create an AI to play battleship
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Howdy, here's the video tutorial as promised!
GitHub: https://github.com/JTexpo/Python_Projects/tree/main/SPD_Battleship
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Neural Network Cost function
GitHub: https://github.com/JTexpo/Python_Projects/blob/main/DNN_Math_Selenium/PythonBot/deep_neural_network.py YouTube Video: https://youtu.be/x2YmEX1XzGI (please forgive the low quality, one of first videos made)
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Your experiences with linear trees
GitHub Code Decision Tree from Scratch: https://github.com/JTexpo/Python_Projects/tree/main/TkMD_Decision_Tree YT Video Decision Trees / Random Forests: https://youtu.be/lrMsO0qGJd4
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Please explain this code of a neural network
DNN from scratch YT link: https://youtu.be/x2YmEX1XzGI (sorry for choppy mic and such, was an early vid) DNN from scratch GitHub: https://github.com/JTexpo/Python_Projects/tree/main/DNN_Math_Selenium
What are some alternatives?
cs229-2019-summer - All notes and materials for the CS229: Machine Learning course by Stanford University
serenity - The Serenity Operating System 🐞
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]
stanford-cs229 - 🤖 Exercise answers to the problem sets from the 2017 machine learning course cs229 by Andrew Ng at Stanford
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.
Coursera-Machine-Learning-Stanford - Machine learning-Stanford University
NNfSiX - Neural Networks from Scratch in various programming languages