{"id":30065829,"url":"https://github.com/ricardorobledo/ml_optimization","last_synced_at":"2026-04-11T07:01:54.783Z","repository":{"id":305363714,"uuid":"1022694477","full_name":"RicardoRobledo/ML_Optimization","owner":"RicardoRobledo","description":null,"archived":false,"fork":false,"pushed_at":"2025-07-19T16:18:53.000Z","size":1582,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-19T20:06:19.750Z","etag":null,"topics":["matplotlib","numpy","python","scikit-learn","xgboost"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RicardoRobledo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-07-19T16:15:12.000Z","updated_at":"2025-07-19T16:18:56.000Z","dependencies_parsed_at":"2025-07-19T20:18:55.967Z","dependency_job_id":null,"html_url":"https://github.com/RicardoRobledo/ML_Optimization","commit_stats":null,"previous_names":["ricardorobledo/ml_optimization"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/RicardoRobledo/ML_Optimization","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Optimization","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Optimization/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Optimization/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Optimization/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RicardoRobledo","download_url":"https://codeload.github.com/RicardoRobledo/ML_Optimization/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RicardoRobledo%2FML_Optimization/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":269378256,"owners_count":24407309,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-08T02:00:09.200Z","response_time":72,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["matplotlib","numpy","python","scikit-learn","xgboost"],"created_at":"2025-08-08T06:38:22.437Z","updated_at":"2026-04-11T07:01:49.743Z","avatar_url":"https://github.com/RicardoRobledo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Optimization for Machine Learning 📈\n\nThis repository contains my personal journey and hands-on implementations focused on **optimization in Machine Learning** — one of the fundamental skills for building efficient, accurate, and well-tuned models.\n\nThe concepts and structure are inspired by the book *Optimization for Machine Learning* available at [machinelearningmastery.com](https://machinelearningmastery.com/), which presents a clear and progressive path to mastering this essential area of ML.\n\n---\n\n## 🧠 What's included in this repository?\n\n- From-scratch implementations of both local and global optimization algorithms.\n- Practical applications of techniques such as:\n  - **Hill Climbing**\n  - **Simulated Annealing**\n  - **Genetic Algorithms**\n  - **Differential Evolution**\n  - **Gradient Descent and variants**: Momentum, RMSProp, AdaGrad, Adadelta, Adam\n- Manual hyperparameter optimization for:\n  - **Perceptron**\n  - **XGBoost**\n  - **Simple Neural Networks**\n- Function and optimization surface visualization.\n- Feature selection using stochastic optimization techniques.\n- Comparisons between methods applied to regression and classification tasks.\n\n---\n\n## 💡 Why is optimization important in Machine Learning?\n\nOptimization is the engine behind model training: it helps us find the best parameters, improve generalization, and reduce error. It's critical for:\n\n- Hyperparameter tuning\n- Feature selection\n- Training deep learning models\n- Function analysis and sensitivity testing\n\n---\n\n## 🛠️ Tools \u0026 Libraries\n\n- `scikit-learn`\n- `xgboost`\n- `matplotlib` for visualization\n- `numpy` \u0026 `scipy`\n\n---\n\n## 🌐 Reference\n\nBook: *Optimization for Machine Learning*  \nWebsite: [https://machinelearningmastery.com](https://machinelearningmastery.com)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fricardorobledo%2Fml_optimization","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fricardorobledo%2Fml_optimization","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fricardorobledo%2Fml_optimization/lists"}