https://github.com/CllsPy/learnAI
A repository chronicling my journey to understand AI, featuring experiments, projects, and lessons learned along the way
https://github.com/CllsPy/learnAI
deep-learning huggingface kaggle-competition machine-learning nlp python pytorch
Last synced: 8 months ago
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A repository chronicling my journey to understand AI, featuring experiments, projects, and lessons learned along the way
- Host: GitHub
- URL: https://github.com/CllsPy/learnAI
- Owner: CllsPy
- Created: 2024-03-12T07:11:50.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-08T20:09:33.000Z (over 1 year ago)
- Last Synced: 2024-12-08T20:28:36.699Z (over 1 year ago)
- Topics: deep-learning, huggingface, kaggle-competition, machine-learning, nlp, python, pytorch
- Language: Jupyter Notebook
- Homepage:
- Size: 18.9 MB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🤖 **Ultra-Learning Artificial Intelligence**
Gostaria de ler este arquivo em [PortuguĂŞs](https://github.com/CllsPy/learnAI/blob/main/extra/others/AprendaIA.md) ?
## Phase 1: Prerequisite-Math Foundations
* Calculus
* **Goals**: Understand derivatives, integrals, and fundamental theorems
* Resources (Choose One):
* Khan Academy: Calculus [Diferential](https://en.khanacademy.org/math/differential-calculus) | [Integral](https://en.khanacademy.org/math/integral-calculus)
* Recommended Books:
* [Calculus Made Easy](https://calculusmadeeasy.org/)
_Note: If you have time, learn about Gradient, it's great to understand how Gradient Descet and BackPropagation Works._
* Linear Algebra
* **Goals**: Master matrix operations, vector spaces, and linear
transformations.
* Resources (choose one):
* Khan Academy: [Linear Algebra](https://en.khanacademy.org/math/linear-algebra)
* Probability and Statistics
* Goals
* Learn probability topics like counting, random variables, mean variance, Bayes’ theorem, distributions, limit theorems
* Learn statistics topics like linear regression, classification,
tree-based methods
* Resources
* Stats: [Learn statistics topics like linear regression, classification,tree-based methods](https://www.youtube.com/playlist?list=PL0IrMnm2latGOFhZTs8UUWz_RXy2NDXdL)
* Khan Academy: [Statistics and Probability](https://www.khanacademy.org/math/statistics-probability)
* All in one course: [Math Of Intelligence](https://www.youtube.com/watch?v=g8D5YL6cOSE&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D&index=2)
## Phase 2: Programming Fundamentals
* Data Structures and Algorithms
* Goals: Learn common data structures and algorithms
* Resources
* [Data Structures and Algorithms in Python - Full Course for Beginners](https://www.youtube.com/watch?v=pkYVOmU3MgA&t=2277s)
* Python
* Goals: Become proficient in Python syntax and libraries.
* Harvard CS50: [Python](https://cs50.harvard.edu/python/2022/)
* Recommended Boks
* Fluent Python: Clear, Concise, and Effective Programming - Luciano Ramalho
## Phase 3: AI Fundamentals
* Machine Learning
* Goals: Understand supervised and unsupervised learning, large language
models, and reinforcement learning.
* Recommended Books
* CS229: [Lecture Notes](https://cs229.stanford.edu/lectures-spring2022/main_notes.pdf)
## Study Log
- [Today I Learnt](https://github.com/CllsPy/Journaling/tree/main)
## Extras
- [Why Machines Learn: The Elegant Math Behind Modern AI](https://www.amazon.fr/Why-Machines-Learn-Elegant-Behind/dp/0593185749)
- [Calculus Made Easy](https://calculusmadeeasy.org/)
- [Machine Learning with PyTorch and Scikit-Learn](https://www.amazon.fr/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319?crid=1BZ1K40TH7BML&dib=eyJ2IjoiMSJ9.9yg8cwnXBFq04RJQdK79SwFjhzjR4fP4EMjh1KmmQLgdBno1pY-FmY5TWxiU6hv_taukDOGmQcsLrfftUrNqcGA0lrI-LFHdqfbLdYC1EJC9m7znegYAWPWvriUf8qjLHwPF_u-RqTU9vU1EDXaLkRXN35N6lvKPU6XPjN8R5NpO7t79t50yRIJRc8AjENa-_fPwgxt93SzNaViU2eQso1odGuCP_7VGhndT_OJUihfzqs7CadZHk7q5oT3Mtc1hPw9XGwt_UlJkBnDuqjl0FrdngPCf1SJKF4-hI2Am9CM.Pjq5rqO0O4__FF5pBpxFo5bKnAGU_WiLT4Plq62xUjE&dib_tag=se&keywords=machine+learning+with+pytorch+and+scikit-learn&qid=1730481361&sprefix=Machine+learning+wi%2Caps%2C325&sr=8-1)
- [Learning From Data](https://work.caltech.edu/telecourse)
- [ML Google For Devs](https://developers.google.com/machine-learning?hl=en)
- Ian Goodfellow and Yoshua Bengio and Aaron Courville
- [Deep Learning Book](https://www.deeplearningbook.com.br/ )
- [StanfordEDx](https://github.com/amaas/stanford_dl_ex)
- [Machine Learning Mastery](https://machinelearningmastery.com/start-here/)
- [AI and DS - Roadmap](https://roadmap.sh/ai-data-scientist)
- [Python Deliberate Practice](https://github.com/robert8138/python-deliberate-practice)
- [Introduction to Deep Learning](https://sebastianraschka.com/blog/2021/dl-course.html#l01-introduction-to-deep-learning)
- [Lil'Log](https://lilianweng.github.io/)
- [Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory](https://arxiv.org/pdf/2310.20360)
- [LLM101n: Let's Build a Storyteller](https://github.com/karpathy/LLM101n?tab=readme-ov-file)
- [The Rise of the AI Engineer](https://www.latent.space/p/ai-engineer)
- [5 Formatting Techniques for Long-Form Content](https://www.nngroup.com/articles/formatting-long-form-content/)
- [Gradient Descent Method](https://pt.khanacademy.org/math/multivariable-calculus/applications-of-multivariable-derivatives/optimizing-multivariable-functions/a/what-is-gradient-descent)
- [Mathematics for Machine Learning](https://mml-book.github.io/)
- [Fast Way to Learn Go](https://www.reddit.com/r/golang/comments/1465pwq/fastest_way_to_learn_golang/)
- [AI Safety](https://80000hours.org/career-reviews/ai-safety-researcher/)
- [Python Documentation Contents](https://docs.python.org/3/contents.html)
- [Data Science vs Data Engineering](https://www.datacamp.com/blog/data-scientist-vs-data-engineer)
- [Calm Code](https://calmcode.io/)
- [Integral Calculus - Khan Academy](https://pt.khanacademy.org/math/integral-calculus)
- [Multivariable Calculus - Khan Academy](https://pt.khanacademy.org/math/multivariable-calculus)
- [The Essence of Calculus - 3B1B](https://www.youtube.com/watch?v=WUvTyaaNkzM&list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)
- [How To Become A 10x Developer: Step-By-Step Guide](https://zerotomastery.io/blog/how-to-become-a-10x-developer/#What-is-a-10x-Developer)
- [How to Become a Machine Learning Engineer: Step-By-Step Guide](https://zerotomastery.io/blog/how-to-become-a-machine-learning-engineer/)
- [How To Become An AI Engineer From Scratch in 2024](https://zerotomastery.io/blog/how-to-become-an-ai-engineer-from-scratch/)
- [Become a Great Software Engineer (Use These 4 Habits)](https://zerotomastery.io/blog/how-to-be-a-great-software-engineer/)
- [Don’t Be a Junior Developer: The Roadmap From Junior to Senior](https://zerotomastery.io/blog/dont-be-a-junior-developer-the-roadmap/)
- [The Prompt Report: A Systematic Survey of Prompting Techniques](https://arxiv.org/pdf/2406.06608)
- [How to Write a Paper](http://halfonlab.ccr.buffalo.edu/other_docs/scientific_paper.pdf)
- [MLE Job Hunt](https://www.yuan-meng.com/posts/newgrads/#tldr)
### Acknowledgements
- [AI and Data Scientist Roadmap](https://roadmap.sh/ai-data-scientist)
- [Roadmap to Learn AI in 2024](https://medium.com/bitgrit-data-science-publication/a-roadmap-to-learn-ai-in-2024-cc30c6aa6e16)
- [ML Engineer Roadmap](https://github.com/chris-chris/ml-engineer-roadmap)
- [Lil](https://lilianweng.github.io/)
- [Neo](https://www.bneo.xyz/)
- [Leonie](https://x.com/helloiamleonie)
- [Gautam Kunapuli](https://gkunapuli.github.io/teaching/)
- [EXA AI](https://cdn.prod.website-files.com/608338f07a8a726c265ad502/67245ae89ec6f0803f08b581_AI%20Roadmap_%20based%20on%20Stanford%20AI%20Graduate%20Certificate.pdf)