{"id":22965512,"url":"https://github.com/CllsPy/learnAI","last_synced_at":"2025-08-13T08:32:28.403Z","repository":{"id":227218595,"uuid":"770795646","full_name":"CllsPy/Ultra-Learning-Ai","owner":"CllsPy","description":"A repository chronicling my journey to understand AI, featuring experiments, projects, and lessons learned along the way","archived":false,"fork":false,"pushed_at":"2024-12-08T20:09:33.000Z","size":19853,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-08T20:28:36.699Z","etag":null,"topics":["deep-learning","huggingface","kaggle-competition","machine-learning","nlp","python","pytorch"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🤖 **Ultra-Learning Artificial Intelligence**\n\nGostaria de ler este arquivo em [Português](https://github.com/CllsPy/learnAI/blob/main/extra/others/AprendaIA.md) ?\n\n## Phase 1: Prerequisite-Math Foundations\n\n* Calculus\n  * **Goals**: Understand derivatives, integrals, and fundamental theorems\n  * Resources (Choose One):\n    * Khan Academy:  Calculus [Diferential](https://en.khanacademy.org/math/differential-calculus) | [Integral](https://en.khanacademy.org/math/integral-calculus)\n  * Recommended Books:\n      *  [Calculus Made Easy](https://calculusmadeeasy.org/)\n\n_Note: If you have time, learn about Gradient, it's great to understand how Gradient Descet and BackPropagation Works._\n\n* Linear Algebra\n  * **Goals**: Master matrix operations, vector spaces, and linear\n  transformations.\n  * Resources (choose one):\n    * Khan Academy: [Linear Algebra](https://en.khanacademy.org/math/linear-algebra)\n\n* Probability and Statistics\n  * Goals\n    * Learn probability topics like counting, random variables, mean variance, Bayes’ theorem,  distributions, limit theorems\n    * Learn statistics topics like linear regression, classification,\ntree-based methods\n  * Resources\n    * Stats: [Learn statistics topics like linear regression, classification,tree-based methods](https://www.youtube.com/playlist?list=PL0IrMnm2latGOFhZTs8UUWz_RXy2NDXdL)\n    * Khan Academy: [Statistics and Probability](https://www.khanacademy.org/math/statistics-probability)\n\n* All in one course: [Math Of Intelligence](https://www.youtube.com/watch?v=g8D5YL6cOSE\u0026list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D\u0026index=2)\n\n## Phase 2: Programming Fundamentals\n* Data Structures and Algorithms\n  * Goals: Learn common data structures and algorithms\n  * Resources\n      * [Data Structures and Algorithms in Python - Full Course for Beginners](https://www.youtube.com/watch?v=pkYVOmU3MgA\u0026t=2277s)\n\n\n* Python\n    * Goals: Become proficient in Python syntax and libraries.\n    * Harvard CS50: [Python](https://cs50.harvard.edu/python/2022/)\n    * Recommended Boks\n        * Fluent Python: Clear, Concise, and Effective Programming - Luciano Ramalho\n\n## Phase 3: AI Fundamentals\n* Machine Learning\n  * Goals: Understand supervised and unsupervised learning, large language\n  models, and reinforcement learning.\n  * Recommended Books\n    * CS229: [Lecture Notes](https://cs229.stanford.edu/lectures-spring2022/main_notes.pdf)\n\n## Study Log\n- [Today I Learnt](https://github.com/CllsPy/Journaling/tree/main)\n\n## Extras\n- [Why Machines Learn: The Elegant Math Behind Modern AI](https://www.amazon.fr/Why-Machines-Learn-Elegant-Behind/dp/0593185749)\n- [Calculus Made Easy](https://calculusmadeeasy.org/)\n- [Machine Learning with PyTorch and Scikit-Learn](https://www.amazon.fr/Machine-Learning-PyTorch-Scikit-Learn-learning/dp/1801819319?crid=1BZ1K40TH7BML\u0026dib=eyJ2IjoiMSJ9.9yg8cwnXBFq04RJQdK79SwFjhzjR4fP4EMjh1KmmQLgdBno1pY-FmY5TWxiU6hv_taukDOGmQcsLrfftUrNqcGA0lrI-LFHdqfbLdYC1EJC9m7znegYAWPWvriUf8qjLHwPF_u-RqTU9vU1EDXaLkRXN35N6lvKPU6XPjN8R5NpO7t79t50yRIJRc8AjENa-_fPwgxt93SzNaViU2eQso1odGuCP_7VGhndT_OJUihfzqs7CadZHk7q5oT3Mtc1hPw9XGwt_UlJkBnDuqjl0FrdngPCf1SJKF4-hI2Am9CM.Pjq5rqO0O4__FF5pBpxFo5bKnAGU_WiLT4Plq62xUjE\u0026dib_tag=se\u0026keywords=machine+learning+with+pytorch+and+scikit-learn\u0026qid=1730481361\u0026sprefix=Machine+learning+wi%2Caps%2C325\u0026sr=8-1)\n\n- [Learning From Data](https://work.caltech.edu/telecourse)\n- [ML Google For Devs](https://developers.google.com/machine-learning?hl=en)\n- Ian Goodfellow and Yoshua Bengio and Aaron Courville\n- [Deep Learning Book](https://www.deeplearningbook.com.br/ )\n- [StanfordEDx](https://github.com/amaas/stanford_dl_ex)\n- [Machine Learning Mastery](https://machinelearningmastery.com/start-here/)\n- [AI and DS - Roadmap](https://roadmap.sh/ai-data-scientist)\n- [Python Deliberate Practice](https://github.com/robert8138/python-deliberate-practice)\n- [Introduction to Deep Learning](https://sebastianraschka.com/blog/2021/dl-course.html#l01-introduction-to-deep-learning)\n- [Lil'Log](https://lilianweng.github.io/)\n- [Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory](https://arxiv.org/pdf/2310.20360)\n- [LLM101n: Let's Build a Storyteller](https://github.com/karpathy/LLM101n?tab=readme-ov-file)\n- [The Rise of the AI Engineer](https://www.latent.space/p/ai-engineer)\n- [5 Formatting Techniques for Long-Form Content](https://www.nngroup.com/articles/formatting-long-form-content/)\n- [Gradient Descent Method](https://pt.khanacademy.org/math/multivariable-calculus/applications-of-multivariable-derivatives/optimizing-multivariable-functions/a/what-is-gradient-descent)\n- [Mathematics for Machine Learning](https://mml-book.github.io/)\n- [Fast Way to Learn Go](https://www.reddit.com/r/golang/comments/1465pwq/fastest_way_to_learn_golang/)\n - [AI Safety](https://80000hours.org/career-reviews/ai-safety-researcher/)\n - [Python Documentation Contents](https://docs.python.org/3/contents.html)\n - [Data Science vs Data Engineering](https://www.datacamp.com/blog/data-scientist-vs-data-engineer)\n - [Calm Code](https://calmcode.io/)\n - [Integral Calculus - Khan Academy](https://pt.khanacademy.org/math/integral-calculus)\n - [Multivariable Calculus - Khan Academy](https://pt.khanacademy.org/math/multivariable-calculus)\n - [The Essence of Calculus - 3B1B](https://www.youtube.com/watch?v=WUvTyaaNkzM\u0026list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)\n - [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)\n - [How to Become a Machine Learning Engineer: Step-By-Step Guide](https://zerotomastery.io/blog/how-to-become-a-machine-learning-engineer/)\n - [How To Become An AI Engineer From Scratch in 2024](https://zerotomastery.io/blog/how-to-become-an-ai-engineer-from-scratch/)\n - [Become a Great Software Engineer (Use These 4 Habits)](https://zerotomastery.io/blog/how-to-be-a-great-software-engineer/)\n - [Don’t Be a Junior Developer: The Roadmap From Junior to Senior](https://zerotomastery.io/blog/dont-be-a-junior-developer-the-roadmap/)\n - [The Prompt Report: A Systematic Survey of Prompting Techniques](https://arxiv.org/pdf/2406.06608)\n - [How to Write a Paper](http://halfonlab.ccr.buffalo.edu/other_docs/scientific_paper.pdf)\n - [MLE Job Hunt](https://www.yuan-meng.com/posts/newgrads/#tldr)\n\n### Acknowledgements\n- [AI and Data Scientist Roadmap](https://roadmap.sh/ai-data-scientist)\n- [Roadmap to Learn AI in 2024](https://medium.com/bitgrit-data-science-publication/a-roadmap-to-learn-ai-in-2024-cc30c6aa6e16)\n- [ML Engineer Roadmap](https://github.com/chris-chris/ml-engineer-roadmap)\n- [Lil](https://lilianweng.github.io/)\n- [Neo](https://www.bneo.xyz/)\n- [Leonie](https://x.com/helloiamleonie)\n- [Gautam Kunapuli](https://gkunapuli.github.io/teaching/)\n- [EXA AI](https://cdn.prod.website-files.com/608338f07a8a726c265ad502/67245ae89ec6f0803f08b581_AI%20Roadmap_%20based%20on%20Stanford%20AI%20Graduate%20Certificate.pdf)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCllsPy%2FlearnAI","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FCllsPy%2FlearnAI","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCllsPy%2FlearnAI/lists"}