{"id":19000311,"url":"https://github.com/jayvatti/mlnotebookarchives","last_synced_at":"2026-04-18T00:03:42.900Z","repository":{"id":174323830,"uuid":"648758056","full_name":"jayvatti/MLNotebookArchives","owner":"jayvatti","description":"My ML/DL Repository ","archived":false,"fork":false,"pushed_at":"2024-11-18T01:08:38.000Z","size":12482,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-23T20:41:16.229Z","etag":null,"topics":["deep-learning","machine-learning","neural-networks","sklearn","tensorflow"],"latest_commit_sha":null,"homepage":"https://jayvatti.github.io/MLNotebookArchives/","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/jayvatti.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,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2023-06-02T18:30:20.000Z","updated_at":"2024-11-18T01:08:42.000Z","dependencies_parsed_at":"2024-11-18T02:19:22.665Z","dependency_job_id":"77ddbe0c-e294-4ebe-80b4-2207f1c7b120","html_url":"https://github.com/jayvatti/MLNotebookArchives","commit_stats":null,"previous_names":["jayvatti/mlnotebookarchives"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jayvatti/MLNotebookArchives","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jayvatti%2FMLNotebookArchives","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jayvatti%2FMLNotebookArchives/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jayvatti%2FMLNotebookArchives/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jayvatti%2FMLNotebookArchives/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jayvatti","download_url":"https://codeload.github.com/jayvatti/MLNotebookArchives/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jayvatti%2FMLNotebookArchives/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31950891,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-17T17:29:20.459Z","status":"ssl_error","status_checked_at":"2026-04-17T17:28:47.801Z","response_time":62,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["deep-learning","machine-learning","neural-networks","sklearn","tensorflow"],"created_at":"2024-11-08T18:06:52.870Z","updated_at":"2026-04-18T00:03:42.844Z","avatar_url":"https://github.com/jayvatti.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# [MLNotebookArchives](https://jayvatti.github.io/MLNotebookArchives/)\n\nThis repository contains a collection of Jupyter notebooks, each highlighting the application and understanding of a distinct machine learning model, from the basics to the more sophisticated. [`In Progress`]\n\n---\n \n## Content\n\n- **[`Data folder`](/data/ReadMe.md):** A curated repository of datasets, serving as the foundation and fuel for the models crafted in these notebooks.\n\n- **[`Kaggle folder`](/kaggle/ReadMe.md):**\n\n## Notebook Structure\n\n### **Basics**\n1. **[`numpyBasics.ipynb`](numpyBasics.ipynb)**\n2. **[`mnist.ipynb`](mnist.ipynb)**\n\n### **Supervised Learning**\n\n1. **[`singleFeatureLinearRegression.ipynb`](singleFeatureLinearRegression.ipynb)**\n    - **Description**: \n        - A beginner's journey into Linear Regression with a single input feature. \n        - Implementation with Sci-kit Learn library.\n        - Another version without any libraries.\n        - Plots of cost functions for learning rate selection.\n        - Bonus: A sketched line of best fit! (With and without Z-Score Normalization)\n    - **Dataset**: [height_weight.csv](/data/height_weight.csv)\u003cbr\u003e\n          \n\n2. **[`multipleFeatureLinearRegression.ipynb`](multipleFeatureLinearRegression.ipynb)**\n    - **Description**: \n        - Stepping up to multi-feature Linear Regression. \n        - Implementation using Sci-kit Learn. \n        - Another version without libraries.\n        - Includes cost function plots to guide the choice of learning rate.\n        - New: Added plots comparing the predictions from the SciKit Learn version and the version without libraries. Also included are plots comparing each method with the actual predictions.\n    - **Dataset**: [multipleFeatures.csv](/data/multipleFeatures.csv) - Because one feature is too mainstream!\u003cbr\u003e\n\n\n\n3. **[`normalEquationLinearRegression.ipynb`](normalEquationLinearRegression.ipynb)**\n    - **Description**:\n        - Ditching iterations for equations, this notebook presents Linear Regression via the Normal equation.\n        - Employs the same dataset as the singleFeatureLinearRegression notebook\n        - Graphical representations may be missing, but the calculated weights and bias are similar to the ones from the gradient descent approach in [singleFeatureLinearRegression.ipnyb](singleFeatureLinearRegression.ipynb)\n    - **Dataset**: [height_weight.csv](/data/height_weight.csv)\u003cbr\u003e\n\n\n4. **[`polynomialRegression.ipynb`](polynomialRegression.ipynb)**\n    - **Description**: Straight lines are too mainstream! This notebook explores Polynomial Regression using Sci-kit Learn.\n    - **Dataset**: [polyRegression.csv](/data/polyRegression.csv) \u003cbr\u003e\n\n\n\n5. **[`binaryLogisticRegression.ipynb`](binaryLogisticRegression.ipynb)**\n    - **Description**:\n        - Flirting with classification, this notebook delves into Binary Logistic Regression using Logistic Loss and Sigmoid Functions.\n        -  Includes a visualization of the sigmoid function and classification boundary, helping to understand how well the model separates classes. Classifying has never been so visually appealing!\n    - **Dataset**: [logisticRegression.csv](/data/logisticRegression.csv)\u003cbr\u003e\n\n\n\n6. **[`regularizedLinearRegression.ipynb`](regularizedLinearRegression.ipynb)**\n    - **Description**: \n        - Regularization to the rescue! This notebook presents Regularized Linear Regression.\n        - Both library-based and bare-bones implementations are available.\n        - Cost function plots to select the best learning rate and lambda values.\n        - New: Added a plot to show the predictions of the custom model versus the original data, and another plot to compare the SciKit Learn version with the version without libraries.\n    - **Dataset**: [multipleFeatures.csv](/data/multipleFeatures.csv) \u003cbr\u003e\n\n\n\n7. **[`regularizedLogisticRegression.ipynb`](regularizedLogisticRegression.ipynb)**\n    - **Description**: \n        - The sequel to regularized linear regression, featuring Regularized Logistic Regression. \n        - Implementations both with and without libraries.\n        - Cost function plots to guide the choice of learning rate and lambda values.\n    - **Dataset**: [logisticRegressionCircular.txt](/data/logisticRegressionCircular.txt)\u003cbr\u003e\n\n\n\n### **Neural Networks**\n\n1. **[`NeuralNetworkLinearRegression.ipynb`](NeuralNetworkLinearRegression.ipynb)**\n    - **Description**: \n        - From regressions to neurons, this notebook implements a single-layer neural network for Linear Regression.\n        - A regression line is sketched to highlight the model's predictions.\n    - **Dataset**: [linearRegressionSimple.csv](/data/linearRegressionSimple.csv) \u003cbr\u003e\n\n\n\n2. **[`NeuralNetworkLogisticRegression.ipynb`](NeuralNetworkLogisticRegression.ipynb)**\n    - **Description**: \n        - Continuing the neural exploration, this notebook applies a single-layer neural network for Logistic Regression. \n        - A sketched sigmoid function after fine-tuning the weights and biases using Sci-kit learn.\n        - Notably, it focuses on tumor classification - the model might not cure cancer, but it sure can classify it!\n    - **Dataset**: [logisticRegressionTumorSingle.csv](/data/logisticRegressionTumorSingle.csv) - On a mission to segregate tumors!\u003cbr\u003e\n\n\n\n3. **[`NeuralNetworkLayers_binaryClassification.ipynb`](NeuralNetworkLayers_binaryClassification.ipynb)**\n    - **Description**: Unleashing the power of deep learning, this notebook explores a multi-layer neural network for binary classification, featuring:\n        - Feature engineering using polynomial features up to degree 6.\n        - Visualizations of activation layers and 3D surface plots. \n        - Building a neural network from scratch, no high-level libraries involved.\n        - New: A 3D Decision Probability Plot, using Plotly - view or interact with it via [nbviewer](https://nbviewer.org/github/jayvatti/MLNotebookArchives/blob/main/NeuralNetworkLayers_binaryClassification.ipynb)\n    - **Dataset**: [logisticRegressionCircular.txt](/data/logisticRegressionCircular.txt)\u003cbr\u003e\n\n\n4. **[`multiClass_softmax.ipynb`](multiClass_softmax.ipynb)**\n    - **Description**: Multiclass classification by employing softmax activation in neural networks. Highlights include:\n        - Comparing models built with and without 'from_logits = true', thus emphasizing the impact of this parameter on neural network performance.\n        - Featuring two different neural network architectures - a simple one with fewer units for a quick yet effective classification, and a moderately large one with more units to generate a more complex decision boundary.\n        - Vivid visualizations of decision boundaries via meshgrid and ravel.\n        - The data exploration journey begins with a custom-generated dataset using 'make_classification' from sklearn.datasets.\n    - **Dataset**: [multi_class_generated.csv](/data/multi_class_generated.csv) - Multi-class problems have never been more fun to solve!\u003cbr\u003e\n\n### **Miscellaneous** \n\n1. **[`dTrees.ipynb`](dTrees.ipynb)**\n   - **Description**:\n\n     \n2. **[`kmeans.ipynb`](kmeans.ipynb)**\n   - **Description**:\n\n     \n3. **[`anomalyDetection.ipynb`](anomalyDetection.ipynb)**\n   - **Description**:\n\n4. **[`pca.ipynb`](pca.ipynb)**\n   - **Description**:\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjayvatti%2Fmlnotebookarchives","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjayvatti%2Fmlnotebookarchives","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjayvatti%2Fmlnotebookarchives/lists"}